Automated ML Techniques for Predicting COVID-19 Mortality in the ICU

Sakagianni, Aikaterini; Koufopoulou, Christina; Kalles, Dimitris; Loupelis, Evangelos; Verykios, Vassilios S.; Feretzakis, Georgios

doi:10.3233/shti230547

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Rahmatinejad et al 7 compared the performance of a variety of regression-based scoring systems, and highlighted the superiority of the APACHE II scoring system in predicting inhospital mortality of patients in the ICU, which was more suitable for critically ill adult patients admitted to the ICU directly from the ED (emergency departments). What sets it apart is the population included in our study, not only the more severe COVID-19 inpatients like in other studies 12 , 23 , 24 but also all COVID-19 inpatients in our hospital. This broad inclusion criterion makes our study less prone to selection bias and improves the generalizability of our findings, as our study has confirmed that our model had higher discrimination in the external dataset demonstrating the generalizability and robustness of the model.…”

Section: Discussionmentioning

confidence: 99%

An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19

Zou,

Yao,

Chen

et al. 2024

Sci Rep

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This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-ZICU: − 0.821 (p-value: 0.412); Spiegelhalter-Zdeath: 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection.

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Section: Discussionmentioning

confidence: 99%

An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19

Zou,

Yao,

Chen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…Additionally, anemia based on age constitutes one of the six specific objective laboratory test findings used to assess suspicions of incomplete Kawasaki disease [ 59 ], underscoring the significance of the Z score of Hb for the corresponding age in incomplete Kawasaki disease. Moreover, combining the Z score of Hb might be superior to Hb with other clinical markers and may enhance the predictive ability for identifying critical illnesses such as COVID-19 mortality in the ICU [ 60 ], sepsis [ 61 ], and liver disease [ 62 ]. Of course, this synergistic approach has the potential to improve the accuracy and reliability of diagnosing and predicting these severe conditions, allowing for timely intervention and appropriate management strategies.…”

Section: Discussionmentioning

confidence: 99%

Hemoglobin and Its Z Score Reference Intervals in Febrile Children: A Cohort Study of 98,572 Febrile Children

Cheng,

Hsu,

Yang

et al. 2023

Children

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Objectives: Febrile disease and age of children were associated with a variation in hemoglobin (Hb) level. Both CRP and Hb serve as laboratory markers that offer valuable insights into a patient’s health, particularly in relation to inflammation and specific medical conditions. Although a direct correlation between CRP and Hb levels is not established, the relationship between these markers has garnered academic attention and investigation. This study aimed to determine updated reference ranges for Hb levels for age and investigated its correlation with CRP in febrile children under the age of 18. Methods: This is a cohort study of in Chang Gung Memorial Hospitals conducted from January 2010 to December 2019. Blood samples were collected from 98,572 febrile children who were or had been admitted in the pediatric emergency department. The parameters of individuals were presented as the mean ± standard deviation or 2.5th and 97.5th percentiles. We also determined the variation of Hb and Z score of Hb between CRP levels in febrile children. Result: We observed that the Hb levels were the highest immediately after birth and subsequently underwent a rapid decline, reaching their lowest point at around 1–2 months of age, and followed by a steady increment in Hb levels throughout childhood and adolescence. In addition, there was a significant and wide variation in Hb levels during the infant period. It revealed a significant association between higher CRP levels and lower Hb levels or a more negative Z score of Hb across all age subgroups. Moreover, in patients with bacteremia, CRP levels were higher, Hb concentrations were lower, and Z scores of Hb were also lower compared to the non-bacteremia group. Furthermore, the bacteremia group exhibited a more substantial negative correlation between CRP levels and a Z score of Hb (r = −0.41, p < 0.001) compared to the non-bacteremia group (r = −0.115, p < 0.049). Conclusion: The study findings revealed that the Hb references varied depending on the age of the children and their CRP levels. In addition, we established new reference values for Hb and its Z scores and explore their relationship with CRP. It provides valuable insights into the Hb status and its potential association with inflammation in febrile pediatric patients.

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Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective

Weaver,

Goodin,

Miller

et al. 2024

Sci Rep

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Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for early clinical interventions, prevention of secondary complications such as nosocomial infections, and effective triage of hospital resources. This study tested the hypothesis that an ensemble machine learning (ML) analysis of clinical data at time of intubation could identify patients at risk of PMV, using a COVID-19 dataset to classify patients into PMV (> 14 days) and non-PMV (≤ 14 days) groups. While several factors are known to cause PMV, including acid-base, weakness, and delirium, lesser-utilized but routinely measured parameters such as platelet count, glucose levels and fevers may also be relevant. Patient data from a single University Hospital were analyzed via the ML workflow to predict patients at risk of PMV and identify key clinical markers. Model performance was evaluated on a chronologically distinct cohort. The ML workflow identified patients at risk of PMV with AUROC TRAIN =0.960 (F1 TRAIN = 0.935) and AUROC TEST =0.804 (F1 TEST = 0.800). Top key features for classification included glucose, platelet count, temperature, LVEF, bicarbonate (arterial blood gas), and BMI. Data analysis at intubation time via the proposed workflow offers the potential to accurately predict patients at risk of PMV, with the goal to improve patient management and triage of hospital resources. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-81980-0.

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Automated ML Techniques for Predicting COVID-19 Mortality in the ICU

Cited by 3 publications

References 3 publications

An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19

An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19

Hemoglobin and Its Z Score Reference Intervals in Febrile Children: A Cohort Study of 98,572 Febrile Children

Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective

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