Care Unit Admission In Patients Research Articles

Multidrug-Resistant Urinary Tract Infections in Pregnant Patients and Their Association with Adverse Pregnancy Outcomes—A Retrospective Study

Background/Objectives: Multidrug-resistant urinary tract infections (MDR UTIs) constitute an important public health problem, especially in pregnant patients. The aim of this retrospective study was to characterize the bacterial spectrum and the profile of microbial resistance in cases of UTIs occurring in pregnant women, as well as their impact on obstetrical and neonatal outcomes. Methods: A total of 371 pregnant patients with UTIs were included in the analysis and were segregated into the following groups based on the type of bacterial resistance to antibiotics: MDR UTIs (70 patients, group 1), UTIs resistant to one class of antibiotics (108 patients, group 2), UTIs resistant to two classes of antibiotics (102 patients, group 3), and sensitive UTIs (91 patients, group 4). We used descriptive statistics for characterizing and comparing the microbial spectrum and the clinical characteristics of the patients. A multinomial logistic regression model for evaluating the relationship between the type of urinary tract infection and adverse obstetric or neonatal outcomes was employed. Results: In the case of MDR UTIs, the bacterial spectrum mainly included Escherichia coli, Enterococcus faecalis, and Klebsiella species. We found almost universal resistance to ampicillin. Our data confirmed an increased risk of preterm birth, premature rupture of membranes, neonatal respiratory distress syndrome, and neonatal intensive care unit admission for patients with MDR infections. Conclusions: The increased incidence of pathogens resistant to commonly used antibiotic classes in pregnancy suggests the need for the development of local and national protocols that adapt therapeutic and prophylactic regimens to clinical realities.

Impact of COVID-19 Vaccination on Fatality and Intensive Care Unit Admission in Patients with Type 2 Diabetes Mellitus

Impact of COVID-19 Vaccination on Fatality and Intensive Care Unit Admission in Patients with Type 2 Diabetes Mellitus

Characteristics of medically attended influenza infection across age groups before the COVID-19 pandemic in Lebanon

BackgroundInfluenza represents a significant global health burden for individuals and society. This study assessed the burden of medically attended influenza at a tertiary medical center in Lebanon to describe the demographics, risk factors, and outcomes prior to the COVID-19 pandemic. MethodsThis was a retrospective review of patients who tested positive for the influenza virus during three seasons between July 1, 2016 to June 30, 2019, at the American University of Beirut Medical Center. ResultsA total of 2049 patients who tested positive for influenza were analyzed. Influenza A accounted for 79.6 % of cases, and influenza B for 19.7 %, with influenza activity starting in October/November and peaking in December/January. Older age above 65 years (AOR=3.584), obesity (AOR=2.183), and chronic conditions such as chronic lung diseases (AOR=1.832), and bacterial co-infection (AOR= 2.834) were found to be independent risk factors for developing complications. Viral co-infection increased the likelihood of death tenfold. Vaccinated patients had a shorter mean hospital stay duration and a lower intensive care unit admission rate. ConclusionThe burden of medically attended influenza at our tertiary medical center in Lebanon prior to the COVID-19 pandemic was high. Vaccination decreased the likelihood of complications leading to intensive care unit admission in patients at risk.

Factors associated with thirty-day mortality and intensive care unit admission in patients undergoing hip fracture surgery.

Various factors contribute to the development of mortality and morbidity in hip fracture surgeries. This study aims to investigate the effects of modifiable factors such as the type of anesthesia, anesthesia management, surgical method, and timing of surgery on 30-day mortality rates, intensive care unit admissions, and complications. A total of 400 patients who underwent hip fracture surgery between January 2021 and December 2023 at a Training and Research Hospital were retrospectively analyzed. Patients were divided into two groups: those followed in the ward, named Group 1 (n=304), and those in the intensive care unit, named Group 2 (n=96). Recorded data included demographic characteristics, American Society of Anesthesiologists (ASA) physical status scores, types of comorbidities, anesthesia type, surgical method, surgical delay time, duration of surgery, blood transfusion requirements, and complications. Patients in Group 2 had higher mean age, comorbidity, and mortality rates compared to Group 1 (p<0.001). In terms of types of comorbidities, the rate of intensive care unit admission was higher in patients with coronary artery disease and chronic renal failure (p<0.001). Mean surgical delay and length of hospital stay were also higher in Group 2 (p<0.001). In multivariate logistic regression analysis, age (p<0.001, Odds Ratio [OR]=1.91, Confidence Interval [CI]=1.046-1.137), ASA score (p<0.001, OR=3.872, CI=1.913-7.838), duration of surgical delay (p<0.001, OR=2.029, CI=1.365-3.017), surgical method (p=0.003, OR=2.003, C=1.258-3.188), and length of hospital stay (p=0.006, OR=1.147, CI=1.04-1.266) were determined as predictive factors for 30-day mortality. This study found that age, ASA classification, length of hospital stay, surgical method, and surgical delay were predictive factors for both morbidity and mortality. Among these, surgical delay time appears to be a modifiable parameter when all factors are considered.

Hospital and regional variations in intensive care unit admission for patients with invasive mechanical ventilation

BackgroundPatients who receive invasive mechanical ventilation (IMV) in the intensive care unit (ICU) have exhibited lower in-hospital mortality rates than those who are treated outside. However, the patient-, hospital-, and regional factors influencing the ICU admission of patients with IMV have not been quantitatively examined.MethodsThis retrospective cohort study used data from the nationwide Japanese inpatient administrative database and medical facility statistics. We included patients aged ≥ 15 years who underwent IMV between April 2018 and March 2019. The primary outcome was ICU admission on the day of IMV initiation. Multilevel logistic regression analyses incorporating patient-, hospital-, or regional-level variables were used to assess cluster effects by calculating the intraclass correlation coefficient (ICC), median odds ratio (MOR), and proportional change in variance (PCV).ResultsAmong 83,346 eligible patients from 546 hospitals across 140 areas, 40.4% were treated in ICUs on their IMV start day. ICU admission rates varied widely between hospitals (median 0.7%, interquartile range 0–44.5%) and regions (median 28.7%, interquartile range 0.9–46.2%). Multilevel analyses revealed significant effects of hospital cluster (ICC 82.2% and MOR 41.4) and regional cluster (ICC 67.3% and MOR 12.0). Including patient-level variables did not change these ICCs and MORs, with a PCV of 2.3% and − 1.0%, respectively. Further adjustment for hospital- and regional-level variables decreased the ICC and MOR, with a PCV of 95.2% and 85.6%, respectively. Among the hospital- and regional-level variables, hospitals with ICU beds and regions with ICU beds had a statistically significant and strong association with ICU admission.ConclusionsOur results revealed that primarily hospital and regional factors, rather than patient-related ones, opposed ICU admissions for patients with IMV. This has important implications for healthcare policymakers planning interventions for optimal ICU resource allocation.

Obesity Surgery Mortality Risk Score as a Predictor for Intensive Care Unit Admission in Patients Undergoing Laparoscopic Bariatric Surgery.

Background: Laparoscopic bariatric surgery provides many benefits including lower postoperative pain scores, reduced opioid consumption, shorter hospital stays, and improved quality of recovery. However, the anaesthetic management of obese patients requires caution in determining postoperative risk and in planning adequate postoperative pathways. Currently, there are no specific indications for intensive care unit (ICU) admission in this surgical population and most decisions are made on a case-by-case basis. The aim of this study is to investigate whether Obesity Surgery Mortality Risk Score (OS-MRS) is able to predict ICU admission in patients undergoing laparoscopic bariatric surgery (LBS). Methods: We retrospectively reviewed data of patients who underwent LBS during a 2-year period (2017-2019). The collected data included demographics, comorbidities and surgery-related variables. Postoperative ICU admission was decided via bariatric anaesthesiologists' evaluations, based on the high risk of postoperative cardiac or respiratory complications. Anaesthesia protocol was standardized. Logistic regression was used for statistical analysis. Results: ICU admission was required in 2% (n = 15) of the 763 patients. The intermediate risk group of the OS-MRS was detected in 84% of patients, while the American Society of Anaesthesiologists class III was reported in 80% of patients. A greater OS-MRS (p = 0.01), advanced age (p = 0.04), male gender (p = 0.001), longer duration of surgery (p = 0.0001), increased number of patient comorbidities (p = 0.002), and previous abdominal surgeries (p = 0.003) were predictive factors for ICU admission. Conclusions: ICU admission in obese patients undergoing LBS is predicted by OS-MRS together with age, male gender, number of comorbidities, previous abdominal surgeries, and duration of surgery.

The Effect of Pulmonary Hypertension on Mortality and Intensive Care Unit Admission in Patients With SARS-CoV-2 Infection: A Systematic Review and Meta-Analysis

Severe COVID-19 can cause acute respiratory distress syndrome, hypoxia, systemic complications, and increased mortality. Pulmonary hypertension (PH) is a major global health issue associated with worsening symptoms and increased mortality. This systematic review aimed to assess the influence of PH onset among COVID-19 patients on all-cause mortality and intensive care unit (ICU) admission. An unrestricted search of five databases up to June 2022 was undertaken. Pulmonary hypertension was assessed using transthoracic echocardiogram, computed tomography, or right heart catheterisation. After duplicate screening, data extraction, and risk of bias assessment, random effects meta-analyses of odds ratios (OR) and their 95% confidence intervals (CI) were performed for all-cause mortality and ICU admission. From the 26 studies that were included (3,373 patients, 76% males, median age 62.6 years), PH in COVID-19 patients was significantly associated with higher odds for all-cause mortality (26 studies; OR 3.89; 95% CI 2.85-5.31; p<0.001) and higher odds for ICU admission (six studies; OR 2.50; 95% CI 1.69-3.70; p<0.001). Meta-regression/subgroup analyses by patient demographics, comorbidities, or therapeutic regimens, and sensitivity analyses did not find any differences. Evidence from observational studies indicates that PH in COVID-19 patients is associated with increased odds of mortality and ICU admission.

Characteristics and Management of Patients with Substance Use Disorders Referred to a Consultation-Liaison Psychiatry Service in Lebanon

IntroductionSubstance use disorders (SUDs) are a growing public health concern in the Arab world. To our knowledge, no previous study in Lebanon assessed the characteristics, management, and outcomes of patients with SUDs seen and managed by a consultation-liaisoin psychiatry (CLP) service.ObjectivesThis study explores the characteristics and management of individuals with SUDs who were referred to the CLP service in a tertiary care center in Lebanon.MethodsAs part of the Consultation-Liaison at the American University of Beirut (CLAUB) analysis, we conducted a retrospective record review of patients referred to our CLP service between February 2019 and May 2020. We assessed differences between SUD and non-SUD consults using Chi-square analysis, Fisher’s exact test, or Mann-Whitney U test, as appropriate.ResultsOf 1475 patients, 278 (18.8%) received a diagnosis of SUD. They were mostly males (73.7%) with an average age of 38.8 years. The most used substances were alcohol (60%) and cannabis (28.4%). Compared to non-SUD consults, patients with SUDs were more likely to be males (odds ratio OR=3.18, p&lt;0.001) and to get intubated during admission (OR=1.81, p=0.048). Predictors of intensive care unit admission in patients with alcohol use disorder included pulmonary or endocrinological disease, benzodiazepine use disorder, and days until CLP referral.ConclusionsThe results of this study highlight the high prevalence of alcohol use among individuals with SUD referred to the CLP service. Additionally, they underscore the limited treatment avenues available in this part of the world. The institution of a comprehensive CLP service is crucial to address the unmet needs of patients with SUDs who present to a general hospital setting.Disclosure of InterestNone Declared

Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning

Background Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP. Methods The 2019 Nationwide Readmission Database was used to identify patients hospitalized with a primary diagnosis of AP without ICU admission. A matched comparison cohort of AP patients with ICU admission within 7 days of hospitalization was identified from the National Inpatient Sample after 1:N propensity score matching. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictors and develop an ICU acute pancreatitis risk (IAPR) score validated by 10-fold cross-validation. Results A total of 1513 patients hospitalized for AP were included. The median age was 50.0 years (interquartile range: 39.0–63.0). The three predictors that were selected included hypoxia (area under the curve [AUC] 0.78), acute kidney injury (AUC 0.72), and cardiac arrhythmia (AUC 0.61). These variables were used to develop a nomogram that displayed excellent discrimination (AUC 0.874) (bootstrap bias-corrected 95% confidence interval 0.824–0.876). There was no evidence of miscalibration (test statistic = 2.88; P = 0.09). For high-risk patients (total score >6 points), the sensitivity was 68.94% and the specificity was 92.66%. Conclusions This supervised machine learning-based model can help recognize high-risk AP hospitalizations. Clinicians may use the IAPR score to identify patients with AP at high risk of ICU admission within the first week of hospitalization.

Clinical prediction models for intensive care unit admission in patients with acute poisoning: is it time for a comprehensive evaluation of their utility?

Clinical prediction models for intensive care unit admission in patients with acute poisoning: is it time for a comprehensive evaluation of their utility?

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions.

The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.

Characteristics and management of patients with substance use disorders referred to a consultation-liaison psychiatry service in Lebanon

BackgroundThis study explores the characteristics and management of individuals with substance use disorders (SUDs) who were referred to the consultation-liaison psychiatry (CLP) service in a tertiary care center in Lebanon. As part of the Consultation-Liaison at the American University of Beirut (CLAUB) analysis, we conducted a retrospective record review of patients referred to our CLP service between February 2019 and May 2020. We assessed differences between SUD and non-SUD consults using chi-square analysis, Fisher’s exact test, or Mann–Whitney U test, as appropriate.ResultsOf 1475 patients, 278 (18.8%) received a diagnosis of SUD. They were mostly males (73.7%) with an average age of 38.8 years. The most used substances were alcohol (60%) and cannabis (28.4%). Compared to non-SUD consults, patients with SUDs were more likely to be males (odds ratio OR = 3.18, p < 0.001) and to get intubated during admission (OR = 1.81, p = 0.048). Predictors of intensive care unit admission in patients with alcohol use disorder included pulmonary or endocrinological disease, benzodiazepine use disorder, and days until CLP referral.ConclusionsThe results of this study highlight the high prevalence of alcohol use among individuals with SUD referred to the CLP service. Additionally, they underscore the limited treatment avenues available in this part of the world. The institution of a comprehensive CLP service is crucial to address the unmet needs of patients with SUDs who present to a general hospital setting.

A nomogram proposal for early prediction of intensive care unit admission in patients with acute antipsychotic poisoning.

Early identification of antipsychotic poisoned patients who may have a potential risk for intensive care unit (ICU) admission is crucial especially when resources are limited. Nomograms were previously used as a practical tool to predict prognosis and planning the treatment of some diseases including some poisoning conditions. However, they were not previously investigated in antipsychotic poisoning. The current study aimed to construct a nomogram to predict the need for ICU admission in acute antipsychotic poisoning. Patients and methods: This 2-year study included 140 patients acutely intoxicated with antipsychotics and admitted at Tanta University Poison Control Centre throughout July 2019 to June 2021. Personal and toxicological data, findings of clinical examination and electrocardiography, as well as, results of laboratory investigations at time of admission were recorded. According to the outcome, patients were divided into ICU-admitted and ICU-not admitted groups. The results of this study provided a proposed nomogram that included five significant independent predictors for ICU admission in acute antipsychotic intoxications; the presence of seizures (OR: 31132.26[108.97-Inf]), corrected QT interval (OR: 1.04[1.01-1.09]), mean arterial blood pressure (OR: 0.83[0.70-0.92]), oxygen saturation (OR: 0.62[0.40 to 0.83)], and Glasgow Coma Scale (OR: 0.25 [0.06-0.56]). It could be concluded that the developed nomogram is a promising tool for easy and rapid decision making to predict the need for ICU admission in acute antipsychotic poisoning.

Pattern of Antibiotic Sensitivity and Resistance in Various Cases of Sepsis in a Mixed Intensive Care Unit set up in a Tertiary Hospital

Introduction: Sepsis has been shown to be one of the critical reasons for Intensive Care Unit admissions of patients for which early and empirical use of antibiotics have been used. Antibiotics resistance is becoming a matter of serious concern, especially in developing countries like ours. In this study, all the cases those were diagnosed with sepsis and had culture positive results for bacteria have been evaluated.&#x0D; Aims: To find out the common organisms responsible for sepsis in patients admitted to the Intensive Care Unit and to know the antibiotic sensitivity/resistance patterns.&#x0D; Methods: The study was done from May 2022 to April 2023 in mixed Intensive Care Units on the clinical isolates from blood, urine, sputum, endotracheal tube aspirate, central venous catheter tips and wound swabs and those were sent to microbiology department for culture and sensitivity. All the organisms were identified morphologically and biochemically and antibiotic susceptibility pattern was determined by standard laboratory procedure.&#x0D; Results: We took a total of 102 samples in which organisms were isolated. The most frequently isolated bacteria were Klebsiella (24.91%) followed by Staphylococcus aureus (24.5%), both were sensitive to Doxycycline and Tazobactam-piperacillin in most cases. Other organisms isolated were Escherichia coli (15.69%), Streptococcus (9.8%), Pseudomonas (8.82%), Acinetobacter (6.86%), Enterobacter species (3.92%) and Proteus (0.98%). Azithromycin was found to be the most resistant drug followed by Amoxycillin/clavulanic acid and Levofloxacin.&#x0D; Conclusion: Klebsiella was found to be the most prevalent cause of sepsis in ICU. Most of the frequently isolated pathogens were resistant to Beta lactamase inhibitors, Cephalosporins and Quinolones. Regular surveillance of antibiotic susceptibility pattern is very important for setting a guideline to the clinician in choosing an appropriate therapy for patients with sepsis.

Development and validation of a prediction model for postoperative intensive care unit admission in patients with non-cardiac surgery

BackgroundAccurately forecasting patients admitted to the intensive care units (ICUs) after surgery may improve clinical outcomes and guide the allocation of expensive and limited ICU resources. However, studies on predicting postoperative ICU admission in non-cardiac surgery have been limited. ObjectiveTo develop and validate a prediction model combining pre- and intraoperative variables to predict ICU admission after non-cardiac surgery. MethodsThis study is based on data from the Vital Signs DataBase (VitalDB) database. Predictors were selected using the least absolute shrinkage and selection operator regression method and logistic regression to develop a nomogram and an online web calculator. The model was internally verified by 1000-Bootstrap resampling. Performance of model was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve and Brier score. The Youden's index was used to find the optimal nomogram's probability threshold. Clinical utility was assessed by decision curve analysis. ResultsThis study included 5216 non-cardiac surgery patients; of these, 812 (15.6%) required postoperative ICU admission. Potential predictors included age, ASA classification, surgical department, emergency surgery, preoperative albumin level, preoperative urea nitrogen level, intraoperative crystalloid, intraoperative transfusion, intraoperative catheterization, and surgical time. A nomogram was constructed with an AUC of 0.917 (95% CI: 0.907–0.926) and a Brier score of 0.077. The Bootstrap-adjusted AUC was 0.914; the adjusted Brier score was 0.078. The calibration curve showed good agreement between predicted and actual probabilities; and the decision curve indicated clinical usefulness. Finally, we established an online web calculator for clinical application (https://xuzhikun.shinyapps.io/postopICUadmission1/). ConclusionWe developed and internally validated an easy-to-use nomogram for predicting ICU admission after non-cardiac surgery.

The predictive value of the modified early warning score for admission to the intensive care unit in patients with a hematologic malignancy – A multicenter observational study

ObjectivesThe modified early warning score (MEWS) is used to detect clinical deterioration of hospitalized patients. We aimed to investigate the predictive value of MEWS and derived quick Sequential Organ Failure Assessment (qSOFA) scores for intensive care unit admission in patients with a hematologic malignancy admitted to the ward. DesignRetrospective, observational study in two Dutch university hospitals. SettingData from adult patients with a hematologic malignancy, admitted to the ward over a 2-year period, were extracted from electronic patient files. Main outcome measuresIntensive care admission. ResultsWe included 395 patients with 736 hospital admissions; 2% (n = 15) of admissions resulted in admission to the intensive care unit. A higher MEWS (OR 1.5; 95 %CI 1.3–1.80) and qSOFA (OR 4.4; 95 %CI 2.1–9.3) were associated with admission. Using restricted cubic splines, a rise in the probability of admission for a MEWS ≥ 6 was observed. The AUC of MEWS for predicting admission was 0.830, the AUC of qSOFA was 0.752. MEWS was indicative for intensive care unit admission two days before admission. ConclusionsMEWS was a sensitive predictor of ICU admission in patients with a hematologic malignancy, superior to qSOFA. Future studies should confirm cut-off values and identify potential additional characteristics, to further enhance identification of critically ill hemato-oncology patients. Implications for Clinical PracticeThe Modified Early Warning Score (MEWS) can be used as a tool for healthcare providers to monitor clinical deterioration and predict the need for intensive care unit admission in patients with a hematologic malignancy. Yet, consistent application and potential reevaluation of current thresholds is crucial. This will enable bedside nurses to more effectively identify patients needing adjunctive care, facilitating timely interventions and improved outcome.

Prognostic Value of CYP1A2 (rs2069514 and rs762551) Polymorphisms in COVID-19 Patients.

The aim of the study was to examine the genotype-allele determination of CYP1A2 rs2069514 and rs762551 polymorphisms in patients with mild and severe COVID-19 and to determine their effectiveness as prognostic criteria in COVID-19. The study consists of 60 patients who were hospitalized in intensive care or outpatient treatment due to COVID-19 in Istanbul NP Brain Hospital between 2020-2021. Genotyping was conducted by Real-Time PCR. Age (p<0.001); chronic disease (p=0.002); cardiovascular disease (p=0.004); respiratory distress (p<0.001); neurological disease (p=0.004); fatigue (p=0.048); loss of taste and smell (p=0.003); nausea/vomiting (p=0.026); intubated (p<0.001); ground glass image (p<0.001) and CYP1A2 genotypes (p<0.001) showed a statistically significant difference between patients with and without intensive care admission. According to multivariate logistic regression analysis, CYP1A2 *1A/*1C + *1C/*1C genotypes (OR:5.23 95% CI: 1.22-22.36; p=0.025), chronic disease (OR:4.68 95% CI:1.14-19.15; p=0.032) or patients at 65 years or older (OR:5.17, 95%CI:1.26-21.14; p=0.022) increased the risk of admission to the intensive care unit. According to our results, we strongly suggest considering the CYP1A2 rs2069514 and rs762551 polymorphisms as important predictors of Intensive Care Unit admission in patients with COVID-19, and we also suggest that genotype results will guide clinicians for the benefit and the efficiency of the treatment.

Platelet-albumin-bilirubin score and neutrophil-to-lymphocyte ratio predict intensive care unit admission in patients with end-stage kidney disease infected with the Omicron variant of COVID-19: a single-center prospective cohort study

Objectives The objective of this study was to develop clinical scores to predict the risk of intensive care unit (ICU) admission in patients with COVID-19 and end stage kidney disease (ESKD). Methods This was a prospective study in which 100 patients with ESKD were enrolled and divided into two groups: the ICU group and the non-ICU group. We utilized univariate logistic regression and nonparametric statistics to analyze the clinical characteristics and liver function changes of both groups. By plotting receiver operating characteristic curves, we identified clinical scores that could predict the risk of ICU admission. Results Out of the 100 patients with Omicron infection, 12 patients were transferred to the ICU due to disease aggravation, with an average of 9.08 days from hospitalization to ICU transfer. Patients transferred to the ICU more commonly experienced shortness of breath, orthopnea, and gastrointestinal bleeding. The peak liver function and changes from baseline in the ICU group were significantly higher, with p values <.05. We found that the baseline platelet-albumin-bilirubin score (PALBI) and neutrophil-to-lymphocyte ratio (NLR) were good predictors of ICU admission risk, with area under curve values of 0.713 and 0.770, respectively. These scores were comparable to the classic Acute Physiology and Chronic Health Evaluation II (APACHE-II) score (p > .05). Conclusion Patients with ESKD and Omicron infection who are transferred to the ICU are more likely to have abnormal liver function. The baseline PALBI and NLR scores can better predict the risk of clinical deterioration and early transfer to the ICU for treatment.

Biochemical, Hematological, and Immunological Biomarkers as Predictors for Intensive Care Unit Admission in Patients with COVID-19.

To identify the biochemical, hematological, and immune biomarkers in COVID-19 patients on admission that are predictive of eventual admission to the intensive care unit (ICU). This retrospective cohort study was conducted on all confirmed COVID-19 cases hospitalized at Royal Hospital, Oman from 24 February to 30 July 2020. The demographic, clinical, and laboratory data were collected from the hospital information system. Patients were divided into two groups: non-ICU admitted group and ICU admitted group. Out of 445 patients, 276 (62.0%) were male and 169 (38.0%) were female; 259 (58.2%) patients were admitted to COVID-19 general wards whereas 186 (41.8%) were admitted to ICU. Admission to ICU was more likely when patient had the following comorbidities: diabetes (OR = 1.8; 95% CI: 1.3-2.7), liver diseases (OR = 2.1; 95% CI: 1.1-4.3), and respiratory diseases (OR = 2.0; 95% CI: 1.1-3.7). Between ICU and non-ICU patients, there were significant differences in on-admission laboratory blood/serum parameters: total white blood cells (WBCs) count, lymphocytes count, C-reactive protein (CRP), ferritin, corrected calcium, interleukin 6 (IL-6), D-dimer, alanine transaminase (ALT), lactate dehydrogenase (LDH), albumin, and troponin. The current study identified the presence of the comorbidities (i.e., diabetes, liver diseases, and respiratory diseases) and on-admission laboratory blood and serum test results (i.e., WBC, lymphocytes, CRP, ferritin, corrected calcium, IL-6, D-dimer, ALT, LDH, albumin, and troponin) that are associated with ICU admission.

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.