Limitations of Only Reporting the Odds Ratio in the Age of Precision Medicine: A Deterministic Simulation Study

Chatterjee, A.; Woodruff, Henry C.; Wu, Guangyao; Lambin, Philippe

doi:10.3389/fmed.2021.640854

Cited by 5 publications

(3 citation statements)

References 17 publications

(17 reference statements)

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“…It is thought to be a good starting point in investigation of the association between binary characteristics. However, this kind of association should not be used to predict outcomes [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

Causes of Death in Neonates, Infants, Children, and Adolescents at the University Children’s Clinical Hospital of Białystok Between 2018 and 2021

Kamianowska

Wasilewska

2023

Med Sci Monit

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Background:Monitoring of mortality rate and causes of death in pediatric hospitals is required in Poland. This study is aimed to evaluate the causes of death in neonates, infants, children, and adolescents obtained from the medical records of the University Children's Clinical Hospital (UCCH) of Białystok between 2018 and 2021. Material/Methods:This was an observational, cross-sectional study. Medical records of 59 patients (12 neonates, 17 infants, 14 children, 16 adolescents) who died in the UCCH of Białystok in 2018-2021 were analyzed. The records included personal data, medical history, and causes of death. Results:Between 2018 and 2021, the leading death causes were congenital malformations, deformations, and chromosomal abnormalities (25.42%, N=15) and conditions originating in the perinatal period (11.86%, N=7). The leading death causes in each age group were: in neonates -congenital malformations, deformations, and chromosomal abnormalities (50%, N=6), in infants -conditions originating in the perinatal period (29.41%, N=5), in children -diseases of the respiratory system (30.77%, N=4), and in teenagers -external causes of morbidity (31%, N=5). Before the COVID-19 pandemic (2018COVID-19 pandemic ( -2019, the leading death causes were congenital malformations, deformations, and chromosomal abnormalities (20.69%, N=6) and conditions originating in the perinatal period (20.69%, N=6). During the COVID-19 pandemic (2020COVID-19 pandemic ( -2021, congenital malformations, deformations, and chromosomal abnormalities (26.67%, N=8) and COVID-19 (10.00%, N=3) were the most common death causes. Conclusions:Leading death causes varied among age groups. The COVID-19 pandemic had an impact on pediatric causes of death and changed their distribution. The results of this analysis should be discussed and conclusions should improve the quality of pediatric care.

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Section: Discussionmentioning

confidence: 99%

Causes of Death in Neonates, Infants, Children, and Adolescents at the University Children’s Clinical Hospital of Białystok Between 2018 and 2021

Kamianowska

Wasilewska

2023

Med Sci Monit

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“…Instead, the standardized ORs that are extracted from the standardized regression coefficients have the same unit and are comparable ( 2 ). Moreover, to compare the prediction accuracy of models, the area under the cure (AUC) is highly suggested ( 3 ).…”

Section: The Higher or Doesn't Necessarily Show The Best Predictormentioning

confidence: 99%

Commentary: Modeling mortality risk in patients with severe COVID-19 from Mexico

Sanjari,

Toosizadeh,

Raeisi Shahraki

2023

Front. Med.

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“…The area under the receiver operating characteristic curve (AUC), unlike Pearson or Spearman correlation, is independent of the class imbalance of a dataset i.e., its value is approximately the same whether a set is balanced between the two outcome classes (mortality and non-mortality, in this case) or not [13,14]. Thus, it was the metric chosen for univariate analysis.…”

Section: Univariate Analysismentioning

confidence: 99%

Improving and Externally Validating Mortality Prediction Models for COVID-19 Using Publicly Available Data

Chatterjee

Wilmink

Woodruff

et al. 2022

BioMed

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We conducted a systematic survey of COVID-19 endpoint prediction literature to: (a) identify publications that include data that adhere to FAIR (findability, accessibility, interoperability, and reusability) principles and (b) develop and reuse mortality prediction models that best generalize to these datasets. The largest such cohort data we knew of was used for model development. The associated published prediction model was subjected to recursive feature elimination to find a minimal logistic regression model which had statistically and clinically indistinguishable predictive performance. This model could still not be applied to the four external validation sets that were identified, due to complete absence of needed model features in some external sets. Thus, a generalizable model (GM) was built which could be applied to all four external validation sets. An age-only model was used as a benchmark, as it is the simplest, effective, and robust predictor of mortality currently known in COVID-19 literature. While the GM surpassed the age-only model in three external cohorts, for the fourth external cohort, there was no statistically significant difference. This study underscores: (1) the paucity of FAIR data being shared by researchers despite the glut of COVID-19 prediction models and (2) the difficulty of creating any model that consistently outperforms an age-only model due to the cohort diversity of available datasets.

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Limitations of Only Reporting the Odds Ratio in the Age of Precision Medicine: A Deterministic Simulation Study

Cited by 5 publications

References 17 publications

Causes of Death in Neonates, Infants, Children, and Adolescents at the University Children’s Clinical Hospital of Białystok Between 2018 and 2021

Causes of Death in Neonates, Infants, Children, and Adolescents at the University Children’s Clinical Hospital of Białystok Between 2018 and 2021

Commentary: Modeling mortality risk in patients with severe COVID-19 from Mexico

Improving and Externally Validating Mortality Prediction Models for COVID-19 Using Publicly Available Data

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