Dynamic profile and clinical implications of hematological and immunological parameters in COVID-19 patients. A retrospective study

BACKGROUND: Coronavirus disease 2019 (COVID-19) may be asymptomatic or symptomatic in pregnant women. Compared to non-pregnant reproductive-aged women, symptomatic individuals appear to have a higher risk of acquiring severe illness sequelae. OBJECTIVES: We assessed the clinical and laboratory characteristics and outcomes of pregnant COVID-19 patients unvaccinated for severe acute respiratory syndrome coronavirus 2 according to the trimester of pregnancy. DESIGN AND SETTING: This was a retrospective observational study conducted in a tertiary-level hospital in Turkey. METHODS: This retrospective study reviewed the clinical and laboratory characteristics and outcomes of 445 pregnant COVID-19 patients hospitalized during the first, second, and third trimesters of pregnancy and 149 other pregnant women as controls in a tertiary center from April 2020 to December 2021. All participants were unvaccinated. RESULTS: Overall, the study groups were comparable in terms of baseline clinical pregnancy characteristics. There was no clear difference among the study participants with COVID-19 in the first, second, and third trimesters of pregnancy. However, a considerably high number of clinical and laboratory findings revealed differences that were consistent with the inflammatory nature of the disease. CONCLUSIONS:The study results reveal the importance of careful follow-up of hospitalized cases as a necessary step by means of regular clinical and laboratory examinations in pregnant COVID-19 patients. With further studies, after implementing vaccination programs for COVID-19 in pregnant women, these data may help determine the impact of vaccination on the outcomes of pregnant COVID-19 patients.

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Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Giotta

Trerotoli

Palmieri

et al. 2022

IJERPH

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Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model’s performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes.

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Dynamic profile and clinical implications of hematological and immunological parameters in COVID-19 patients. A retrospective study

Cited by 5 publications

References 20 publications

The Impact of Severe COVID–19 on Corneal Endothelial Cells–Analysis of the In Vivo Noncontact Specular Microscopy

The Impact of Severe COVID–19 on Corneal Endothelial Cells–Analysis of the In Vivo Noncontact Specular Microscopy

Short-term outcomes of COVID-19 in pregnant women unvaccinated for SARS-CoV-2 in the first, second, and third trimesters: a retrospective study

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

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