Sepehr Shahriarirad

Background: Complementary and Integrative Medicine (CIM) is often taken up by individuals seeking relief from different diseases. This study investigates the prevalence and associated factors of CIM use in patients with COVID-19.Methods: In this telephone-based, cross-sectional study, data on CIM usage were collected from COVID-19 patients from February till June 2020 in Fars province, Iran using a researcher-made checklist. Additionally, we asked about the patients’ attitudes toward these treatments.Results: Out of 453 patients diagnosed with COVID-19, 400 (88.30%) responded to our calls and agreed to participate in the study. Among them, 276 patients reported using CIM to treat COVID-19 [prevalence: 69% (95% CI: 64.2 to 73.5)]. The most frequently used herbal medicine among COVID-19 patients was ginger (n = 273, 98.9%), thyme (n = 263, 95.3%), and black cumin (n = 205, 74.3%). Most of these patients were recommended to use herbal medicine by their families and friends (n = 96, 34.8%). Univariable logistic regression revealed that age under 50 years old, residency in urban areas (including the capital of the province and small cities), employment, academic education, and being an outpatient were statistically significant factors resulting in CIM usage. Multivariable logistic regression revealed that CIM use among outpatients was 3.65 times more than among inpatients. In addition, patients under 50 years old used CIM 85% more than older patients. Ultimately, only 9 (3.3%) patients consulted with their doctors regarding these medications. No side effects due to CIM use were reported.Conclusion: Many patients with COVID-19 used CIM, but few consulted with their physicians in this regard. Therefore, physicians should ask their patients about CIM usage, and patients should also report their use of CIM therapies during their medical visits. Furthermore, age and hospitalization status affected CIM use among patients with COVID-19.

Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models. A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O₂ saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors. In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients’ early data, particularly in low- and middle-income countries where their resources are as limited as Iran. Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022;26(6):688–695. This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018).

Aim: This study aimed to introduce a 3D printed, flexible esophageal stent, which is useful in personalized treatment of patients with esophageal strictures and also reduce the adverse effects of t...