Outcomes in Severe COVID-19 Patients Following Percutaneous Versus Open Surgical Tracheostomy: An Analysis of Clinical and Prognostic Indicators.

Background and objective Tracheostomy in coronavirus disease 2019 (COVID-19) patients can be performed in cases of prolonged intubation or in patients with a known difficult airway. Tracheostomy is usually performed by two main approaches: open surgery or percutaneous endoscopic insertion. However, few studies have compared these two techniques in severe COVID-19 patients. The objective of the present study was to compare the efficacy of the two main methods of tracheostomy among patients with severe COVID-19 infection. We also aimed to investigate the impact of various lab data and medications on patient outcomes. Materials and methods We included all symptomatic severe COVID-19 patients in need of prolonged mechanical ventilation. We examined the patients' past medical history, arterial blood gas (ABG) analysis, laboratory workups, and medication history. We calculated the PaO2/FiO2 ratio as an index to evaluate the severity of acute respiratory distress syndrome (ARDS). Results During the study period, 72 patients with severe COVID-19 underwent tracheostomy tube insertion. The average age of participants was 58.93 ±15.27 years; 44 (61.1%) were male and 28 (38.9%) were female. Of note, 54 (75.0%) patients passed away and only 18 (25.0%) survived. Among the survivors, 13 (29.5%) were men and five (17.9%) were women. The study showed a significantly higher mortality rate (23, 92.0%) among patients who underwent open surgery compared to those who received percutaneous surgery (31, 65.9%) (p=0.01). Conclusion Based on our findings, percutaneous endoscopic tracheostomy seems to be the superior approach compared to open tracheostomy. Other predictive factors associated with patient outcomes included levels of HCO3, FiO2, PaCO2, and PaO2/FiO2 ratio.

Predictors of Intensive Care Unit Admission in Patients with Confirmed Coronavirus Disease 2019: A Cross-Sectional Study.

Background: The coronavirus disease 2019 (COVID-19) has become the leading source of pneumonia outbreaks in the world. The present study aimed to compare the condition of intensive care unit (ICU) and non-ICU COVID-19 patients in terms of epidemiological and clinical features, laboratory findings, and outcomes in three cities across Iran. Methods: In a cross-sectional study, 195 COVID-19 patients admitted to five hospitals across Iran during March-April 2020 were recruited. Collected information included demographic data, laboratory findings, symptoms, medical history, and outcomes. Data were analyzed using SPSS software with t test or Mann-Whitney U test (continuous data) and Chi square test or Fisher's exact test (categorical variables). P<0.05 was considered statistically significant. Results: Of the 195 patients, 57.4% were men, and 67.7% had at least one comorbidity. The prevalence of stroke, chronic obstructive pulmonary disease, and autoimmune diseases was higher in ICU than in non-ICU patients (P=0.042, P=0.020, and P=0.002, respectively). Compared with non-ICU, ICU patients had significantly higher white blood cell (WBC) count (P=0.008), cardiac troponin concentrations (P=0.040), lactate dehydrogenase levels (P=0.027), erythrocyte sedimentation rates (P=0.008), and blood urea nitrogen (BUN) (P=0.029), but lower hematocrit levels (P=0.001). The mortality rate in ICU and non-ICU patients was 48.1% and 6.1%, respectively. The risk factors for mortality included age>40 years, body mass index<18 Kg/m2, hypertension, coronary artery disease, fever, cough, dyspnea, ST-segment changes, pericardial effusion, and a surge in WBC and C-reactive protein, aspartate aminotransferase, and BUN. Conclusion: A high index of suspicion for ICU admission should be maintained in patients with positive clinical and laboratory predictive factors.

Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran.

Background: Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). Aims and objectives: To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. Materials and methods: 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. Results: 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. Conclusion: 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. How to cite this article: 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. Ethics approval: This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018).

Methylprednisolone or dexamethasone, which one is superior corticosteroid in the treatment of hospitalized COVID-19 patients: a triple-blinded randomized controlled trial.

BACKGROUND: Although almost a year has passed since the Coronavirus disease 2019 (COVID-19) outbreak and promising reports of vaccines have been presented, we still have a long way until these measures are available for all. Furthermore, the most appropriate corticosteroid and dose in the treatment of COVID-19 have remained uncertain. We conducted a study to assess the effectiveness of methylprednisolone treatment versus dexamethasone for hospitalized COVID-19 patients. METHODS: In this prospective triple-blinded randomized controlled trial, we enrolled 86 hospitalized COVID-19 patients from August to November 2020, in Shiraz, Iran. The patients were randomly allocated into two groups to receive either methylprednisolone (2 mg/kg/day; intervention group) or dexamethasone (6 mg/day; control group). Data were assessed based on a 9-point WHO ordinal scale extending from uninfected (point 0) to death (point 8). RESULTS: There were no significant differences between the groups on admission. However, the intervention group demonstrated significantly better clinical status compared to the control group at day 5 (4.02 vs. 5.21, p = 0.002) and day 10 (2.90 vs. 4.71, p = 0.001) of admission. There was also a significant difference in the overall mean score between the intervention group and the control group, (3.909 vs. 4.873 respectively, p = 0.004). The mean length of hospital stay was 7.43 ± 3.64 and 10.52 ± 5.47 days in the intervention and control groups, respectively (p = 0.015). The need for a ventilator was significantly lower in the intervention group than in the control group (18.2% vs 38.1% p = 0.040). CONCLUSION: In hospitalized hypoxic COVID-19 patients, methylprednisolone demonstrated better results compared to dexamethasone. TRIAL REGISTRATION: The trial was registered with IRCT.IR (08/04/2020-No. IRCT20200204046369N1 ).