Ultrasonographic evaluation of diaphragm thickness and excursion: correlation with weaning success in trauma patients: prospective cohort study.

PURPOSE: Prolonged mechanical ventilation (MV) subjects multiple trauma patients to ventilator-induced diaphragmatic dysfunction. There is limited evidence on the predictive role of diaphragm ultrasound (DUS) for weaning success in multiple trauma patients. Therefore, we evaluated Ultrasound of the diaphragm as a valuable indicator of weaning outcomes, in trauma patients. MATERIAL AND METHODS: This prospective cohort study included 50 trauma patients from September 2018 to February 2019. DUS was performed twice: upon ICU admission and the first weaning attempt. The diagnostic accuracy of indexes was evaluated by ROC curves. RESULTS: The study included patients with a mean age of 35.4 ± 17.37, and 78% being male. The median injury severity score was 75 (42-75). The failure group exhibited significantly lower right diaphragmatic excursion (DE) compared to the success group (P = 0.006). In addition, the failure group experienced a significant decrease in both right and left DE from admission to the first attempt of weaning from MV (P < 0.001). Both groups showed a significant decrease in inspiratory and expiratory thickness on both sides during weaning from MV compared to the admission time (P < 0.001). The findings from the ROC analysis indicated that the Rapid shallow breathing index (RSBI) (Sensitivity = 91.67, Specificity = 100), respiratory rate (RR)/DE (Right: Sensitivity = 87.5, Specificity = 92.31), and RR/TF (Thickening Fraction) (Right: Sensitivity = 83.33, Specificity = 80.77) demonstrated high sensitivity and specificity in predicting weaning outcome. CONCLUSION: In the context of patients with multiple trauma, employing DUC and assessing diaphragmatic excursion, thickness, RR/DE index, RR/TF index, and RSBI can aid in determining successful ventilator weaning.

Unintended impact of COVID-19 pandemic on the rate of catheter related nosocomial infections and incidence of multiple drug resistance pathogens in three intensive care units not allocated to COVID-19 patients in a large teaching hospital.

BACKGROUND: The prevalence of resistant hospital infections in the intensive care unit (ICU) increases mortality and antibiotic resistance. COVID-19 pandemic may have unintended impact on nosocomial infections (NI) and the prevalence of resistant microorganism. METHODOLOGY: The present non-interventional study was performed by a pre and a post survey each lasting 8 months before (March-October 2019) and after (March-October 2020) the onset of COVID-19 pandemic in three ICU's, not allocated to COVID-19 patients, in Nemazee Hospital, Shiraz, Iran. The rates of the following nosocomial infections were compared at pre- and post-pandemic period: ventilator associated pneumonia (VAP), central line associated blood stream infection (CLABSI), catheter-associated urinary tract infections (CAUTI) and incidence of multiple drug resistance (MDR) pathogens. RESULTS: Pre-pandemic and pandemic incidence of VAP was 23.5 and 17.2 cases per 1000 device-days, respectively; an absolute decrease of 27%. The main reason for the decrease in the rate of VAP during the pandemic was a significant decrease in the rate of VAP caused by Acinetobacter baumannii; from 39 to 17% in total VAP episodes. The rate of VAP associated with other microorganisms remained relatively unchanged from 14.2 cases in pre-pandemic period to 14.3 cases per 1000 MV-days during the pandemic (P = 0.801). Pre-pandemic incidence of CLABSI was 7.3 cases and, in pandemic period, was 6.5 cases per 1000 device-days (IRR = 0.88, 95% CI 0.43-1.73, P = 0.703). Pre-pandemic incidence of CAUTI was 2 and in pandemic period, was 1.4 cases per 1000 device-days (IRR = 0.70, 95% CI 0.22-1.98, P = 0.469). CONCLUSION: The results of the present study showed a decrease in the incidence of VAP in critically ill non-COVID-19 patients during the pandemic compared to before the pandemic, especially regarding Acinetobacter baumannii.

The Effectiveness of the Intermediate and Therapeutic Doses of Enoxaparin in COVID-19 Patients: A Comparative Study of Factor Xa Inhibition.

BACKGROUND: Management of anticoagulant therapy in COVID-19 patients is critical. Low-molecular-weight heparin (LMWH) thromboprophylaxis is already recommended, and anti-Factor Xa (anti-FXa) monitoring has been used to titrate LMWH doses. METHODS: Through a cross-sectional study, we evaluated anti-FXa activity in patients admitted to the ICU, receiving intermediate dose (30, 40, 50 mg, subcutaneously [SC], twice daily) or therapeutic dose (1 mg/kg, SC, Q12h) of enoxaparin to find whether the patients in these two groups achieved anti-FXa levels in the accepted thromboprophylaxis range. RESULTS: The occurrence of deep vein thrombosis was 26% in the therapeutic-dose group and 17% in the intermediate-dose group. D-dimer values were nearly 3.5-fold higher in those who received a therapeutic dose of anticoagulants than in those who received intermediate-dose thromboprophylaxis. Patients in the therapeutic-dose group had significantly higher IL-6 levels (p ≤ 0.001). More than one-third of the patients in the therapeutic-dose group (n = 8; 42.18%) and approximately half of the patients in the intermediate-dose group (n = 12; 52.2%) achieved the target range level of anti-FXa. Patients who received therapeutic doses were more likely to have anti-FXa levels above the expected range (47.4 vs 13% in the intermediate-dose group; p < 0.05). CONCLUSION: Therapeutic dose of enoxaparin in critically ill COVID-19-infected patients did not reduce the incidence of thromboembolic events and, on the other hand, may predispose these patients to increased risk of bleeding by increasing anti-FXa activity above the desired level. Administration of intermediate-dose thromboprophylaxis is suggested to achieve anti-FXa levels in the accepted thromboprophylaxis range.

Development and evaluation of an anesthesia module for electronic medical records in the operating room: an applied developmental study.

Developing an anesthesia module in the operating room is one of the significant steps toward the implementation of electronic medical records (EMR) in health care centers. This study aimed to develop and evaluate the web based-anesthesia module of an electronic medical record Sciences, in the operating room of the Namazi Medical Training Center of Shiraz University of Medical Iran. This developmental and applied study was conducted in steps including determining the functional and non-functional requirements, designing and implementing the anesthesia module, and usability evaluation. 3 anesthesiologists, 3 anesthesiologist assistants, and 12 anesthetist nurses were included in the study as a research community. React.js, Node.js programming language to program this module, Mongo dB database, and Windows server for data management and USE standard questionnaire were used. In the anesthesia module, software quality features were determined as functional requirements and non-functional requirements included 286 data elements in 25 categories (demographic information, surgery information, laboratory results, patient graphs, consults, consent letter, physical examinations, medication history, family disease records, social record, past medical history, type of anesthesia, anesthesia induction method, airway management, monitoring, anesthesia chart, blood and fluids, blood gases, tourniquets and warmers, accessories, positions, neuromuscular reversal, transfer the patient from the operating room, complications of anesthesia and, seal/ signature). Also, after implementing the anesthesia module, results of the usability evaluation showed that 69.1% of the users agreed with the use of this module in the operating room and considered it user-friendly.

Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems.

BACKGROUND: One of the challenging decision-making tasks in healthcare centers is the interpretation of blood gas tests. One of the most effective assisting approaches for the interpretation of blood gas analysis (BGA) can be artificial intelligence (AI)-based decision support systems. A primary step to develop intelligent systems is to determine information requirements and automated data input for the secondary analyses. Datasets can help the automated data input from dispersed information systems. Therefore, the current study aimed to identify the data elements required for supporting BGA as a dataset. MATERIALS AND METHODS: This cross-sectional descriptive study was conducted in Nemazee Hospital, Shiraz, Iran. A combination of literature review, experts' consensus, and the Delphi technique was used to develop the dataset. A review of the literature was performed on electronic databases to find the dataset for BGA. An expert panel was formed to discuss on, add, or remove the data elements extracted through searching the literature. Delphi technique was used to reach consensus and validate the draft dataset. RESULTS: The data elements of the BGA dataset were categorized into ten categories, namely personal information, admission details, present illnesses, past medical history, social status, physical examination, paraclinical investigation, blood gas parameter, sequential organ failure assessment (SOFA) score, and sampling technique errors. Overall, 313 data elements, including 172 mandatory and 141 optional data elements were confirmed by the experts for being included in the dataset. CONCLUSIONS: We proposed a dataset as a base for registries and AI-based systems to assist BGA. It helps the storage of accurate and comprehensive data, as well as integrating them with other information systems. As a result, high-quality care is provided and clinical decision-making is improved.

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).

Epidemiology of candidemia in Shiraz, southern Iran: A prospective multicenter study (2016-2018).

Systematic candidemia studies, especially in southern Iran, are scarce. In the current prospective study, we investigated candidemia in three major healthcare centers of Shiraz, the largest city in southern Iran. Yeast isolates from blood and other sterile body fluids were identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and subjected to antifungal susceptibility testing (AFST) using the broth microdilution method. Clinical data were retrieved from patients' medical records. In total, 113 yeast isolates were recovered from 109 patients, over 60% of whom received fluconazole. Antifungal drugs were prescribed without considering species identification or AFST. The all-cause mortality rate was 28%. Almost 30% of the patients were from intensive care units (ICUs). Candida albicans (56/113; 49.5%) was the most prevalent species followed by C. glabrata (26/113; 23%), C. parapsilosis (13/113; 11.5%), C. tropicalis (7/113; 6.2%), and C. dubliniensis (5/113; 4.4%). Only five isolates showed antifungal resistance or decreased susceptibility to fluconazole: one C. orthopsilosis isolate from an azole-naïve patient and two C. glabrata, one C. albicans, and one C. dubliniensis isolates from patients treated with azoles, who developed therapeutic failure against azoles later. Our results revealed a low level of antifungal resistance but a notable rate of azole therapeutic failure among patients with candidemia due to non-albicans Candida species, which threaten the efficacy of fluconazole, the most widely used antifungal in southern regions of Iran. Candidemia studies should not be confined to ICUs and treatment should be administered based on species identification and AFST results.

Landscape of candidemia is blurred in Iran, and only two studies from Tehran have extensively explored the epidemiology of candidemia. However, candidemia data from the other regions are notoriously scarce, which precludes from reaching a consensus regarding species distribution, the burden of antifungal resistance, and the clinical features of infected patients. Therefore, we conducted the current prospective candidemia study in Shiraz, one of the largest cities located in the south of Iran, from April 2016 to April 2018. More than 63% of the candidemia infections were treated by fluconazole and species identification and antifungal susceptibility testing were not used for decision making regarding the choice of antifungal treatment. Approximately 70% of the candidemia cases occurred in the wards outside of the ICUs. Candida albicans, C. glabrata, C. parapsilosis, C. tropicalis, and C. dubliniensis were the five leading causative agents of candidemia. Antifungal resistance was rare and fluconazole resistance and/or non-wild type phenotypes were noticed in five isolates, only one was C. albicans and the rest were non-albicans Candida (NAC) species, including C. glabrata, C. dubliniensis, and C. orthopsilosis. Except for C. orthopsilosis, which was isolated from an azole-naïve patient, the rest of isolates were recovered from patients treated with azoles and all showed therapeutic failure to azoles. Collectively, our data will complete the candidemia picture in Iran and show that, although the level of resistance was rare, the therapeutic failure was notable among NAC species, which threatens the efficacy of fluconazole, the most widely used antifungal in Southern regions of Iran. Moreover, we showed that candidemia is poorly managed in Iran since species identification tools along with antifungal susceptibility testing were not used to select appropriate antifungal treatment.

Adequacy of maternal anesthesia depth with two sodium thiopental doses in elective caesarean section: a randomized clinical trial.

BACKGROUND: Administration of an optimal dose of anesthetic agent to ensure adequate depth of hypnosis with the lowest risk of adverse effects to the fetus is highly important in cesarean section. Sodium thiopental (STP) is still the first choice for induction of anesthesia in some countries for this obstetric surgery. We aimed to compare two doses of STP with regarding the depth of anesthesia and the condition of newborn infants. METHODS: In this clinical trial, parturient undergoing elective Caesarian section were randomized into two groups receiving either low-dose (5 mg/kg) or high-dose (7 mg/kg) STP. Muscle relaxation was provided with succinylcholine 2 mg/kg and anesthesia was maintained with O2/N2O and sevoflurane. The depth of anesthesia was evaluated using isolated forearm technique (IFT) and bispectral index (BIS) in various phases. Additionally, infants were assessed using Apgar score and neurobehavioral test. RESULTS: Forty parturient were evaluated in each group. BIS was significantly lower in high-dose group at skin incision to delivery and subcutaneous and skin closure. Also, significant differences were noticed in IFT over induction to incision and incision to delivery. Apgar score was significantly lower in high-dose group at 1 min after delivery. Newborn infants in low-dose group had significantly better outcomes in all three domains of the neurobehavioral test. CONCLUSION: 7 mg/kg STP is superior to 5 mg/kg in creating deeper hypnosis for mothers. However, it negatively impacts Apgar score and neurobehavioral test of neonates. STP seems to has dropped behind as an acceptable anesthetic in Cesarean section. TRIAL REGISTRATION: IRCT No: 2016082819470 N45 , 13/03/2019.

Clinical and microbiological features of candiduria in critically ill adult patients in Shiraz, Iran (2016-2018): deviations from international guidelines and fluconazole therapeutic failure.

Candiduria is common among patients admitted to intensive care units (ICUs); however, clinical and microbiological data are limited, which accounts for non-compliance with international guidelines, including over treatment of asymptomatic candiduria that promotes antifungal resistance. This prospective study included adult patients admitted to ICUs of five referral hospitals in Shiraz, Iran, during 2016-2018. Species were identified by MALDI-TOF MS, and antifungal susceptibility was assessed according to CLSI M27-A3/S4. Among 2086 patients, 162 and 293 developed candiduria and bacteriuria, respectively. In total, 174 yeast isolates were collected; 88.5% were Candida albicans (91/174; 52.2%), C. glabrata (38/174; 21.8%), and C. tropicalis (25/174; 14.3%). Antifungal resistance was rare; only two isolates (one C. tropicalis and one C. krusei) were fluconazole resistant. Symptomatic candiduria was noted in 31.4% of patients (51/162); only 37% (19/51) of them were treated and 36.82% (7/19) showed fluconazole therapeutic failure. Two symptomatic patients developed candidemia shortly after candiduria. Among asymptomatic patients, 31.5% (35/111) were overtreated with fluconazole. The mortality rate was 25.3% (41/162); it did not differ between symptomatic and asymptomatic patients. Our results indicate that deviation from standard-of-care treatment for candiduria is a matter of concern given the high rate of fluconazole therapeutic failure among patients with symptomatic candiduria. LAY SUMMARY: Candiduria is an underestimated clinical presentation among critically ill patients and detailed data are scarce in this regard. Given the high rate of fluconazole therapeutic failure and development of candidemia in some cases, the mistreatment of candiduria should not be overlooked by clinicians.

A nurses' alarm fatigue questionnaire: development and psychometric properties.

Alarm fatigue can adversely affect nurses' efficiency and concentration on their tasks, which is a threat to patients' safety. The purpose of the present study was to develop and test the psychometric accuracy of an alarm fatigue questionnaire for nurses. This study was conducted in two stages: in stage one, in order to establish the different aspects of the concept of alarm fatigue, the researchers reviewed the available literature-articles and books-on alarm fatigue, and then consulted several experts in a meeting to define alarm fatigue and develop statements for the questionnaire. In stage two, after the final draft had been approved, the validity of the instrument was measured using the two methods of face validity (the quantitative and qualitative approaches) and content validity (the qualitative and quantitative approaches). Test-retest, Cronbach's alpha, and Principal Component Analysis were used for item reduction and reliability analysis. Based on the results of stage one, the researchers extracted 30 statements based on a 5-point Likert scale. In stage two, after the face and content validity of the questionnaire had been established, 19 statements were left in the instrument. Based on factor loadings of the items and "alpha if item deleted" and after the second round of consultation with the expert panel, six items were removed from the scale. The test of the reliability of nurses' alarm fatigue questionnaire based on the internal homogeneity and retest methods yielded the following results: test-retest correlation coefficient = 0.99; Guttman split-half correlation coefficient = 0.79; Cronbach's alpha = 0.91. Regarding the importance of recognizing alarm fatigue in nurses, there is need for an instrument to measure the phenomenon. The results of the study show that the developed questionnaire is valid and reliable enough for measuring alarm fatigue in nurses.