Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers

Boss, Anna N.; Banerjee, Abhirup; Mamalakis, Michail; Ray, Surajit; Swift, Andrew J.; Wilkie, Craig; Fanstone, Joseph W.; Vorselaars, Bart; Weeks, Simonne; Mackenzie, Louise

doi:10.3390/ijms23137260

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Some studies described the subject of decreased albumin as an independent factor for mortality in COVID-19, as well as its association with severe forms of infection, cytokine storm, advanced age, and multiple-organ failure [66,67]. In other specialized studies, serum urea, d-dimer, and troponin levels have been mentioned as negative prognostic factors in COVID-19, along with male gender, advanced age, and the presence of type 2 diabetes [68,69].…”

Section: Discussionmentioning

confidence: 99%

Serum Calcium and Magnesium Levels in Patients with Type 2 Diabetes and COVID-19 Infection Requiring Hospitalization—Correlations with Various Parameters

Reștea,

Tigan,

Fritea

et al. 2024

Microbiology Research

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The purpose of this research was to analyze the impact of SARS-CoV-2 infection on ionic calcium, total calcium and serum magnesium upon hospital admission, taking into account the association of type 2 diabetes as a metabolic comorbidity. Our study included 57 patients: a group of 28 patients without diabetes, but with SARS-CoV-2 virus infection, and a second group of 29 patients with type 2 diabetes and SARS-CoV-2 virus infection. The serum level of calcium and magnesium of the patients included in the study did not differ statistically significantly in those with type 2 diabetes compared to those without type 2 diabetes who were infected with the SARS-CoV-2 virus at the time of hospitalization. Ionic calcium, total calcium, and serum magnesium did not statistically significantly influence the survival of the patients with COVID-19 infection included in this research, but the type of infection severity (mild or moderate) did influence the survival rate. Concerning the diabetic patients, a statistically significant correlation was found between serum total calcium and total serum proteins, and another one between ionic calcium and uric acid, urea, and total cholesterol. Serum total calcium and D-dimers were statistically significantly correlated with being transferred to the intensive care unit. On the other hand, magnesium significantly correlated with lipids (triglycerides, total lipids) and inflammatory (fibrinogen, ESR) biomarkers.

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Section: Discussionmentioning

confidence: 99%

Serum Calcium and Magnesium Levels in Patients with Type 2 Diabetes and COVID-19 Infection Requiring Hospitalization—Correlations with Various Parameters

Reștea,

Tigan,

Fritea

et al. 2024

Microbiology Research

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Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning

Mamalakis,

Banerjee,

Ray

et al. 2024

Neural Comput & Applic

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The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems.

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Functional principal component analysis and sparse-group LASSO to identify associations between biomarker trajectories and mortality among hospitalized SARS-CoV-2 infected individuals

Cao,

Reeder,

Foulkes

2023

BMC Med Res Methodol

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Background A substantial body of clinical research involving individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evaluated the association between in-hospital biomarkers and severe SARS-CoV-2 outcomes, including intubation and death. However, most existing studies considered each of multiple biomarkers independently and focused analysis on baseline or peak values. Methods We propose a two-stage analytic strategy combining functional principal component analysis (FPCA) and sparse-group LASSO (SGL) to characterize associations between biomarkers and 30-day mortality rates. Unlike prior reports, our proposed approach leverages: 1) time-varying biomarker trajectories, 2) multiple biomarkers simultaneously, and 3) the pathophysiological grouping of these biomarkers. We apply this method to a retrospective cohort of 12, 941 patients hospitalized at Massachusetts General Hospital or Brigham and Women’s Hospital and conduct simulation studies to assess performance. Results Renal, inflammatory, and cardio-thrombotic biomarkers were associated with 30-day mortality rates among hospitalized SARS-CoV-2 patients. Sex-stratified analysis revealed that hematogolical biomarkers were associated with higher mortality in men while this association was not identified in women. In simulation studies, our proposed method maintained high true positive rates and outperformed alternative approaches using baseline or peak values only with respect to false positive rates. Conclusions The proposed two-stage approach is a robust strategy for identifying biomarkers that associate with disease severity among SARS-CoV-2-infected individuals. By leveraging information on multiple, grouped biomarkers’ longitudinal trajectories, our method offers an important first step in unraveling disease etiology and defining meaningful risk strata.

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Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers

Cited by 3 publications

References 26 publications

Serum Calcium and Magnesium Levels in Patients with Type 2 Diabetes and COVID-19 Infection Requiring Hospitalization—Correlations with Various Parameters

Serum Calcium and Magnesium Levels in Patients with Type 2 Diabetes and COVID-19 Infection Requiring Hospitalization—Correlations with Various Parameters

Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning

Functional principal component analysis and sparse-group LASSO to identify associations between biomarker trajectories and mortality among hospitalized SARS-CoV-2 infected individuals

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