Predictors Of Intensive Care Unit Research Articles

Early predictors of intensive care unit admission among COVID-19 patients in Qatar.

COVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19. This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender. A total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001). Having cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.

Clinical characteristics and mortality prediction of patients admitted to the Hong Kong East Cluster intensive care units in the COVID‐19 fifth wave

AbstractIntroductionCOVID‐19 has been threatening global public health since 2019. To address the overwhelming caseload, several tools were developed to predict prognosis and aid triage of critically ill patients for intensive care. Currently, there is a lack of local data on the validity of such tools.ObjectiveThe objective of this study is to identify the predictors of intensive care unit (ICU) mortality in COVID‐19 patients in Hong Kong and externally validate the different scoring systems.MethodsA retrospective cohort analysis of patients admitted to the HKEC ICUs from 1 January 2022 to 30 April 2022 was performed. We collected data on patient demographics, vaccination status, laboratory parameters, and clinical outcomes including need for organ support and mortality. Clinical severities were estimated by Sequential Organ Failure Score, 4C Mortality Score, COVID‐Gram score, and Acute Physiology and Chronic Health Evaluation (APACHE) IV score based on the original studies. Comparison between individual scoring systems' performance on hospital mortality was conducted and summarized.ResultsIn these four months, 137 patients with COVID‐19 admitted to ICUs of Ruttonjee Hospital and Pamela Youde Nethersole Eastern Hospital were recruited. 64 patients (46.7%) were admitted for COVID‐19 related respiratory failure, among which mortality was 66.7%. The overall hospital and ICU mortality were 21.9% and 13.1%, respectively. Invasive mechanical ventilation (IMV) (OR 3.221, p 0.034), high flow nasal cannula (HFNC) oxygen therapy (OR 3.107, p 0.039), and higher APACHE IV score (OR 1.043, p &lt; 0.001) were independent predictors of hospital mortality using multivariate analysis. The scoring systems had good performance in mortality prediction in our population. The APACHE IV score (AUROC 0.79, 95% CI 0.698–0.894) and 4C Mortality Score (AUROC 0.751, 95% CI 0.657–0.844) outperformed other scoring systems in predicting hospital mortality.ConclusionIn patients with COVID‐19, the use of IMV or HFNC and APACHE IV score were independent risk factors for hospital mortality. The APACHE IV and the 4C Mortality Score were most useful in our population for predicting ICU and hospital mortality.

Shock index in the emergency department as a predictor for mortality in COVID-19 patients: A systematic review and meta-analysis

BackgroundThe shock index (SI) ratio serves as a straightforward predictor to identify patients who are either at risk of or experiencing shock. COVID-19 patients with shock face increased mortality risk and reduced chances of recovery. This review aims to determine the role of SI in the emergency department (ED) to predict COVID-19 patient outcomes. MethodsThe systematic search was conducted in PubMed, ProQuest, Scopus, and ScienceDirect on June 16, 2023. We included observational studies evaluating SI in ED and COVID-19 patient outcomes. Random-effect meta-analysis was done to generate odds ratios of SI as the predictor of intensive care unit (ICU) admission and mortality. The sensitivity and specificity of SI in predicting these outcomes were also pooled, and a summary receiver operating characteristics (sROC) curve was generated. ResultsA total of eight studies involving 4557 participants were included in the pooled analysis. High SI was found to be associated with an increased risk of ICU admission (OR 5.81 [95%CI: 1.18–28.58], p = 0.03). Regarding mortality, high SI was linked to higher rates of in-hospital (OR 7.45 [95%CI: 2.44–22.74], p = 0.0004), within 30-day (OR 7.34 [95%CI: 5.27–10.21], p < 0.00001), and overall (OR 7.52 [95%CI: 3.72–15.19], p < 0.00001) mortality. The sensitivity and specificity of SI for predicting ICU admission were 76.2% [95%CI: 54.6%–89.5%] and 64.3% [95%CI: 19.6%–93.0%], respectively. In terms of overall mortality, the sensitivity and specificity were 54.0% (95%CI: 34.3%–72.6%) and 85.9% (95%CI: 75.8%–92.3%), respectively, with only subtle changes for in-hospital and within 30-day mortality. Adjustment of SI cut-off to >0.7 yielded improved sensitivity (95%CI: 78.0% [59.7%–89.4%]) and specificity (95%CI: 76.8% [41.7%–93.9%]) in predicting overall mortality. ConclusionSI in emergency room may be a simple and useful triage instrument for predicting ICU admission and mortality in COVID-19 patients. Future well-conducted studies are still needed to corroborate the findings of this study.

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database.

There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality. The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency. Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map. Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics. Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

Predictors of ICU Admission in Children with COVID-19: Analysis of a Large Mexican Population Dataset.

Children, although mostly affected mildly or asymptomatically, have also developed severe coronavirus disease 2019 (COVID-19). This study aims to assess potential predictors of intensive care unit (ICU) admission in a large population (n = 21,121) of children aged 0-9 years with laboratory-confirmed disease. We performed a cross-sectional analysis of a publicly available dataset derived from the normative epidemiological surveillance of COVID-19 in Mexico. The primary binary outcome of interest was admission to the ICU due to respiratory failure. Results showed that immunosuppressed children and those with a personal history of cardiovascular disease had a higher likelihood of being admitted to the ICU, while increasing age and the pandemic duration were associated with a lower likelihood of admission. The study's results have the potential to inform clinical decision-making and enhance management and outcomes for children affected by COVID-19 in Mexico.

Evaluation of various scoring systems as predictors of the need for intensive care unit admission and other adverse outcomes among patients with acute clozapine poisoning.

Acute clozapine poisoning (ACP) is frequently reported worldwide. We evaluated the efficacy of the Poison Severity Score (PSS), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Rapid Emergency Medicine Score (REMS), and Modified Early Warning Score (MEWS) as predictors for intensive care unit (ICU) admission, mechanical ventilation (MV), mortality, and length of hospital stay in patients with ACP. A retrospective cohort study was conducted using records of patients diagnosed with ACP from January 2017 to June 2022 and admitted to an Egyptian poison control center. Analyzing 156 records showed that all assessed scores were significant predictors of the studied outcomes. The PSS and APACHE II score showed the highest area under the curve (AUC) as ICU admission predictors with insignificant variations. The APACHE II score showed the best discriminatory power in predicting MV and mortality. Nevertheless, MEWS exhibited the highest odds ratio (OR) as an ICU predictor (OR=2.39, and 95% confidence interval=1.86-3.27) and as a mortality predictor (OR=1.98, and 95% confidence interval=1.16-4.41). REMS and MEWS were better predictors of length of hospital stay compared with the APACHE II score. The simpler, lab-independent nature and the comparable discrimination but higher odds ratio of MEWS compared with APACHE II score justify MEWS' superior utility as an outcome predictor in ACP. We recommend using either the APACHE II score or MEWS, depending on the availability of laboratory investigations, resources, and the case's urgency. Otherwise, the MEWS is a substantially feasible, economical, and bedside alternative outcome predictor in ACP.

Estimating predictors of severity of group A Streptococcus infection in pregnancy

Objective To identify the clinical characteristics of pregnancy associated group A streptococcus (GAS) infection and predictors for intensive care unit (ICU) admission. Methods A retrospective cohort study of culture-proven pregnancy-related GAS infections in tertiary hospital Electronic medical records were reviewed, for cases of cultures positive GAS that were identified between January 2008 and July 2021. A GAS infection was defined by the isolation of the pathogen from a sterile liquid or tissue site. Blood and urine cultures were obtained from all patients with peripartum hyperpyrexia (fever >38 °C). Medical Personnel screening included cultures of the throat, rectum, and skin lesions (if present). In cases of hemodynamic instability patients were transferred ad hoc to ICU, according to the obstetrician and intensivist judgment. Results Of the 143,750 who delivered during the study period, 66 (0.04%) were diagnosed as having a pregnancy associated GAS infection. Of these, 57 patients presented postpartum, and represented the study cohort. The most common presenting signs and symptoms among puerperal GAS, were postpartum pyrexia (72%), abdominal pain (33%), and tachycardia (>100 bpm, 22%). 12 women (21.0%) developed streptococcal toxic shock syndrome (STSS. Predictors for STSS and ICU admission were: antibiotic administration >24 h from presentation postpartum, tachycardia, and a C-reactive protein level >200 mg/L. Women that received antibiotic prophylaxis during labor had a significantly lower rate of STSS (0 vs 10, 22.7%; p = .04). Conclusion Deferral of medical intervention >24 h from the first registered abnormal sign had the most important impact on deterioration of women with invasive puerperal GAS. Antibiotic prophylaxis during labor in women with GAS may reduce associated complications.

Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data

IntroductionOptimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic. MethodsIn this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation. ResultsThe study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU; 1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores. Discussion and conclusionsThe authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources.

Predictors of Intensive Care Admission in Hypoxemic Bronchiolitis Infants, Secondary Analysis of a Randomized Trial

To evaluate in a preplanned secondary analysis of our parent randomized controlled trial predictors of intensive care unit (ICU) admission in infants with bronchiolitis and analyze if these predictors are equally robust for children receiving high-flow or standard-oxygen. A secondary analysis of a multicenter, randomized trial of infants aged <12months with bronchiolitis and an oxygen requirement was performed using admission and outcome data of all 1472 enrolled infants. The primary outcome was ICU admission. The predictors evaluated were baseline characteristics including physiological data and medical history. Of the 1472 enrolled infants, 146 were admitted to intensive care. Multivariate predictors of ICU admission were age (weeks) (OR: 0.98 [95% CI: 0.96-0.99]), pre-enrolment heart rate >160/min (OR: 1.80 [95% CI: 1.23-2.63]), pre-enrolment SpO2 (transcutaneous oxygen saturation) (%) (OR: 0.91 [95% CI: 0.86-0.95]), previous ICU admission (OR: 2.16 [95% CI: 1.07-4.40]), and time of onset of illness to hospital presentation (OR: 0.78 [95% CI: 0.65-0.94]). The predictors were equally robust for infants on high-flow nasal cannula therapy or standard-oxygen therapy. Age <2months, pre-enrolment heart rate >160/min, pre-enrolment SpO2 of <87%, previous ICU admission and time of onset of ≤2days to presentation are predictive of an ICU admission during the current hospital admission of infants with bronchiolitis independent of oxygenation method used. ACTRN12613000388718.

Non-invasive predictors of ICU admission and mortality in initially asymptomatic COVID-19 patients

BackgroundCoronavirus disease 2019 (COVID-19) can present with pulmonary and non-pulmonary manifestations, or it may be asymptomatic. Asymptomatic patients have a major impact on transmission of the disease, and prediction of their outcome and prognosis is challenging. We aim to identify the predictors of intensive care unit (ICU) admission and mortality in hospitalized COVID-19 patients with initially asymptomatic presentation.MethodsThis was a prospective multicenter study using cohort data that included all admitted patients aged 21 years and above, with different clinical presentations other (than pulmonary manifestation) and were discovered to have COVID-19. Demographic data, clinical data and progression were reported. Univariate analysis and logistic regression analysis were performed to predict ICU admission and mortality during hospitalization.ResultsOne hundred forty-nine consecutive patients, 92 (61.7% males) were included in our study, Median age (IQR) was 59.00 (43–69]. Only 1 patient (0.7%) had a contact with a confirmed case of COVID-19. 58 patients (39%) were admitted to ICU and 22 patients (14.8%) have died. High ferritin level (more than 422.5), low oxygen saturation (less than 93%), and in need of non-invasive ventilation (NIV) have 3.148, 8.159 and 26.456 times likelihood to be admitted to ICU, respectively. Patients with high CO-RADS, low oxygen saturation (less than 92.5%), and in need for mechanical ventilation (MV) have 82.8, 15.9, and 240.77 times likelihood to die, respectively.ConclusionInitially asymptomatic hospitalized patients with COVID-19 have a great impact on health system with high ICU admission and mortality rate. We identified the predictors that may help in early management and improving prognosis.Trial registrationTrial was registered in Clinicaltrials.gov, registration number is NCT05298852, 26 March 2022, retrospectively registered.

The Predictive Score for Patients Hospitalized With COVID-19 in Resource-Limited Settings

Background and aims The second wave of coronavirus disease 2019 (COVID-19) has been devastating in India and many developing countries. The mortality reported has been 40% higher than in the first wave, overwhelming the nation's health infrastructure. Despite a better understanding of the disease and established treatment protocols including steroids and heparin, the second wave was disastrous. Subsequent waves have the potential to further cripple healthcare deliveries, also affecting non-COVID-19 care across many developing economies. It is then important to identify and triage high-risk patients to best use the limited resources. Routine tests such as neutrophil and monocyte counts have been identified but have not been successfully validated uniformly, and their utility is still being understood in COVID-19. Various predictive models that are availablerequire online resources and calculators and additionally await validation across all populations. These, although useful, might not be available or accessible across all institutions. It is then important to identify easy-to-use scores that utilize tests done routinely. In identifying with this goal, we did a retrospective review of the institutional database to identify potential predictors of intensive care unit (ICU) admission and mortality in patients hospitalized during the second wave who accessed healthcare at our academic setup. Results Three predictors of mortality and four predictors of ICU admission were identified. Absolute neutrophil count was a common predictor of both ICU admission and mortality but with two separate cut points. An absolute neutrophil count of >4,200 predicted need for ICU admission (odds ratio (OR): 3.1 (95% confidence interval (CI): 2.0, 4.8)), and >7,200 predicted mortality (adjusted OR: 4.2 (95% CI: 1.9, 9.4)). We observed that a blood urea level greater than 45 was predictive of needing ICU care (adjusted OR: 8.0 (95% CI: 3.7, 17.6)). In our dataset, serum ferritin of >500 was predictive of ICU admission (adjusted OR: 2.7 (95% CI: 1.2, 5.9)). We noted a right shift of partial pressure (p50is the oxygen tension at which hemoglobin is 50% saturated) (p50c) in SARS-CoV-2 as a predictor of ICU care (OR: 2.6 (95% CI: 1.7, 3.9)) when partial pressure is >26.5. In our analysis, a serum protein of less than 7 g/dL (OR: 2.8 (95% CI: 1.7, 4.4)) was a predictive variable for ICU admission. An LDH value of >675 was predictive of severity with a need for ICU admission (OR: 9.2 (95% CI: 5.4, 15.5)) in our series. We then assigned a score to each of the predictive variables based on the adjusted odds ratio. Conclusion We identified a set of easy-to-use predictive variables and scores to recognize the subset of patients hospitalized with COVID-19 with the highest risk of death or clinical worsening requiring ICU care.

Characteristics and Early Predictors of Intensive Care Unit Admission among COVID-19 Patients in Qatar

PurposeThis study aimed to explore the early predictors of intensive care unit (ICU) admission and in-hospital mortality among patients diagnosed with Coronavirus disease (COVID-19).Methods & MaterialsThis was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29 - May 29, 2020. For each case enrolled, one control was matched by age and gender. Univariate and multivariate logistic regression models were used to identify the predictors for ICU admission and in-hospital mortality among the COVID‐19 patients.ResultsA total of 1560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range, IQR=18). Predictors independently associated with ICU admission included having cardiovascular disease (CVD) (adjusted odds ratio (aOR)=1.64, 95% confidence interval (CI): 1.16 - 2.32, p= 0.005), diabetes (aOR=1.52, 95% CI: 1.08 - 2.13, p= 0.016), body mass index ≥30 kg/m2 (aOR=1.46, 95% CI: 1.03-2.08, p= 0.034), lymphocytes ≤0.8 × 103/μL (aOR=2.69, 95% CI: 1.80-4.02, p<0.001), aspartate aminotransferase (AST) >120 U/L (aOR= 2.59, 95% CI: 1.53-4.36, p<0.001), ferritin >600 μg/L (aOR=1.96, 95% CI: 1.40-2.74, p<0.001), C-reactive protein (CRP) >100 mg/L (aOR=4.09, 95% CI: 2.81-5.96, p<0.001), and dyspnea (aOR=2.50, 95% CI: 1.77-3.54, p <0.001). Similarly, significant predictors of mortality included CVD (aOR=2.16, 95% CI: 1.32- 3.53, p=0.002), diabetes (aOR=1.77, 95% CI: 1.07-2.90, p=0.025), cancer (aOR=4.65, 95% CI: 1.50-14.42, p= 0.008), lymphocytes ≤0.8 x,103/μL (aOR=2.34, 95% CI: 1.45-3.78, p= 0.001), and AST >120 U/L (aOR= 1.89, 95% CI: 1.04-3.43, p=0.036).ConclusionHaving CVD, diabetes, lymphopenia, and increased AST were independent predictors for both ICU admission and in-hospital mortality in patients with COVID-19. In addition, obesity, high ferritin, and CRP levels were also associated with increased risk of ICU admission, while cancer was strongly associated with in-hospital mortality. Early identification and monitoring of patients at risk is essential in planning the level of care needed to prevent delay in medical intervention.

A Hard Day at Work: An Analysis of Occupational Genitourinary Injuries in the United States Workforce

ObjectiveTo use national data to identify risk factors for occupational genitourinary (GU) injuries and to expose potential workplace safety issues requiring national regulation. Materials and MethodsThe National Trauma Data Bank was queried to identify all adults who suffered a work-related GU injury from 2007 to 2016. Injury was stratified by individual organ and by organ type: intra-abdominopelvic (IAP) vs external genitalia (EG). Distinct multivariable logistic regression models were used to examine associations between prespecified risk factors and GU injury (organ and type) and to identify predictors of intensive care unit and operating room (OR) transfer. ResultsTwo thousand one hundred thirty-nine patients (total of 2681 GU injuries), were included. A mean of 1.3 GU organ injuries and 7.6 total injuries were suffered per patient. 72% suffered an IAP GU injury, 23% an EG injury, and 5% suffered both. Patients working in agriculture/forestry/fishing, (OR 2.3, P = .003), manufacturing (OR 1.9, P = .05), and natural resources/mining (OR 2.3, P = .012) were at significantly increased risk of EG injury. The penis and urethra were particularly at-risk in agriculture/forestry/fishing (OR 4.0, P = .005; OR 3.0, P = .002) and the urethra in natural resources/mining (OR 3.4, P = .004). IAP GU injury was a significant predictor of intensive care unit transfer (OR 1.8, P <.001), whereas EG injury was a significant predictor of OR transfer (OR 2.5, P <.001). ConclusionOccupational GU injuries remain a major issue for blue-collar workers. External genitalia are particularly at-risk, and injuries often require emergent surgery. National occupational health agencies need to continue to enhance on-the-job safety for those at-risk.

395. Early Predictors of Intensive Care Unit Admission among COVID-19 Patients in Qatar

BackgroundCoronavirus disease (COVID-19) is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission and in-hospital mortality among patients diagnosed with COVID-19.MethodsThis was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29 - May 29, 2020. For each case enrolled, one control was matched by age and gender. ResultsA total of 1560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (CVD) (adjusted odds ratio (aOR)=1.64, 95% confidence interval (CI): 1.16 - 2.32, p=0.005), diabetes (aOR=1.52, 95% CI: 1.08 - 2.13, p= 0.016), obesity (aOR=1.46, 95% CI: 1.03-2.08, p= 0.034), lymphopenia (aOR=2.69, 95% CI: 1.80-4.02, p< 0.001), high aspartate aminotransferase (AST) (aOR= 2.59, 95% CI: 1.53-4.36, p< 0.001), high ferritin (aOR=1.96, 95% CI: 1.40-2.74, p< 0.001), high C-reactive protein (CRP) (aOR=4.09, 95% CI: 2.81-5.96, p< 0.001), and dyspnea (aOR=2.50, 95% CI: 1.77-3.54, p< 0.001). Similarly, significant predictors of mortality included CVD (aOR=2.16, 95% CI: 1.32- 3.53, p=0.002), diabetes (aOR=1.77, 95% CI: 1.07-2.90, p=0.025), cancer (aOR=4.65, 95% CI: 1.50-14.42, p= 0.008), lymphopenia (aOR=2.34, 95% CI: 1.45-3.78, p= 0.001), and high AST (aOR= 1.89, 95% CI: 1.04-3.43, p=0.036).Risk Factors for ICU admission among patients with COVID-19 (N=1560) ConclusionHaving CVD, diabetes, lymphopenia, and increased AST were independent predictors for both ICU admission and in-hospital mortality in patients with COVID-19. In addition, obesity, high ferritin, and CRP levels were associated with increased risk of ICU admission, while cancer was strongly associated with in-hospital mortality. Early identification and monitoring of patients at risk is essential in planning the level of care needed to prevent delay in medical intervention.Disclosures Adel Abou-Ali, PharmD, PhD, Astellas Pharma Global Development, Inc. (Employee)

Factors associated with poor outcomes among hospitalized patients with COVID-19: Experience from a MERS-CoV referral hospital

BackgroundCoronavirus disease 2019 (COVID-19) has resulted in millions of deaths, including more than 6000 deaths in the Kingdom of Saudi Arabia (KSA). Identifying key predictors of intensive care unit (ICU) admission and mortality among infected cases would help in identifying individuals at risk to optimize their care. We aimed to determine factors of poor outcomes in hospitalized patients with COVID-19 in a large academic hospital in Riyadh, KSA that serves as a Middle East Respiratory Syndrome coronavirus (MERS-CoV) referral center. MethodsThis is a single-center retrospective cohort study of hospitalized patients between March 15 and August 31, 2020. The study was conducted at King Saud University Medical City (KSUMC). COVID-19 infection was confirmed using real-time reverse transcriptase polymerase chain reaction (RT-PCR) for SARS-COV-2. Demographic data, clinical characteristics, laboratory, radiological features, and length of hospital stay were obtained. Poor outcomes were, admission to ICU, need for invasive mechanical ventilation (IMV), and in-hospital all-cause mortality. ResultsOut of 16,947 individuals tested in KSUMC, 3480 (20.5%) tested positive for SARS-CoV-2 and of those 743 patients (21%) were hospitalized. There were 62% males, 77% were younger than 65 years. Of all cases, 204 patients (28%) required ICU admission, 104 (14%) required IMV, and 117 (16%) died in hospital. In bivariate analysis, multiple factors were associated with mortality among COVID-19 patients. Further multivariate analysis revealed the following factors were associated with mortality: respiratory rate more than 24/min and systolic blood pressure <90 mmHg in the first 24h of presentation, lymphocyte count <1 × 109/L and aspartate transaminase level >37 units/L in the first 48 h of presentation, while a RT-PCR cycle threshold (Ct) value ≤24 was a predictor for IMV. ConclusionVariable factors were identified as predictors of different outcomes among COVID-19 patients. The only predictor of IMV was a low initial Ct values of SARS-CoV-2 PCR. The presence of tachypnea, hypotension, lymphopenia, and elevated AST in the first 48h of presentation were independently associated with mortality. This study provides possible independent predictors of mortality and invasive mechanical ventilation. The data may be helpful in the early identification of high-risk COVID-19 patients in areas endemic with MERS-CoV.

Predictors of Intensive Care Unit Admission or Mortality in Patients with Coronavirus Disease 2019 Pneumonia in Istanbul, Turkey.

We aimed to determine the predictors of intensive care unit (ICU) admission or death in patients with coronavirus disease 2019 (COVID-19) pneumonia. This retrospective, single-center study included patients aged ≥18 years who were diagnosed with COVID-19 pneumonia (laboratory and radiologically confirmed) between March 9 and April 8, 2020. The composite endpoint was ICU admission or in-hospital mortality. Univariate and multivariate logistic regression analyses were performed to evaluate the factors associated with the composite endpoint. A total of 336 patients with COVID-19 pneumonia were evaluated. The median age was 54 years (interquartile range: 21), and 187 (55.7%) were men. Fifty-one (15.2%) patients were admitted to the ICU. In-hospital mortality occurred in 33 patients (9.8%). In the univariate analysis, 17 parameters were associated with the composite endpoint, and procalcitonin had the highest odds ratio (odds ratio [OR] = 36.568, confidence interval [CI] = 5.145-259.915). Our results revealed that body temperature (OR = 1.489, CI = 1.023-2.167, P = 0.037), peripheral capillary oxygen saturation (SpO2) (OR = 0.835, CI = 0.773-0.901, P < 0.001), and consolidation (> 25%) on chest computed tomography (OR = 3.170, CI = 1.218-8.252, P = 0.018) at admission were independent predictors. As a result, increased body temperature, decreased SpO2, a high level of procalcitonin, and degree of consolidation on chest computed tomography may predict a poor prognosis and have utility in the management of patients.

Gender differences in COVID-19 patients: a regional survey among physicians of Internal Medicine Wards

Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) has infected millions of individuals around the World. Hypertension (HT), chronic heart disease (CHD), and diabetes mellitus (DM), particularly in the elderly, increase susceptibility to SARS-CoV-2 infection. However, conflicting results [such as coronavirus 2019 (COVID-19) disease vulnerability, case fatality, etc.] have been reported about the response to infection and COVID-19 outcomes in men and women. Therefore, understanding predictors of Intensive Care Unit (ICU) admission might help future planning and management of the disease. We conducted a multicenter survey about COVID-19 involving internists from Internal Medicine Wards. This survey indirectly allowed us to analyze the information of 2400 patients hospitalized in 35 wards of Internal Medicine of the Campania Region between July and October 2020. Our investigation has detected that the infection is more frequent in males, and the number of male patients hospitalized in ICU is also higher than females, with a large proportion of hypertensive patients. Extensive prospective studies are required to confirm this finding and explore the mechanisms for which hypertensive males are exposed to a higher proportion of admission to ICU and higher case fatality rates.

Transcatheter arterial embolization for severe blunt liver injury in hemodynamically unstable patients: a 15-year retrospective study

BackgroundTranscatheter arterial embolization (TAE) is the first-line nonsurgical treatment for severe blunt liver injury in patients, whereas operative management (OM) is recommended for hemodynamically unstable patients. This study investigated the comparative efficacy of TAE in hemodynamically unstable patients who responded to initial infusion therapy.MethodsThis retrospective study enrolled patients with severe blunt liver injuries, which were of grades III–V according to the American Association for the Surgery of Trauma Organ Injury Scale (OIS). Patients who responded to initial infusion therapy underwent computed tomography to determine the treatment plan. A shock index > 1, despite undergoing initial infusion therapy, was defined as hemodynamic instability. We compared the clinical outcomes and mortality rates between patients who received OM and those who underwent TAE.ResultsSixty-two patients were included (eight and 54 who underwent OM and TAE, respectively; mean injury severity score, 26.6). The overall in-hospital mortality rate was 6% (13% OM vs. 6% TAE, p = 0.50), and the hemodynamic instability was 35% (88% OM vs. 28% TAE, p < 0.01). Hemodynamically unstable patients who underwent TAE had 7% in-hospital mortality and 7% clinical failure. Logistic regression analysis showed that the treatment choice was not a predictor of outcome, whereas hemodynamic instability was an independent predictor of intensive care unit stay ≥7 days (odds ratio [OR], 3.80; p = 0.05) and massive blood transfusion (OR, 7.25; p = 0.01); OIS grades IV–V were predictors of complications (OR, 6.61; p < 0.01).ConclusionsTAE in hemodynamically unstable patients who responded to initial infusion therapy to some extent has acceptable in-hospital mortality and clinical failure rates. Hemodynamic instability and OIS, but not treatment choice, affected the clinical outcomes.

Variations in Presentation and Management of COVID-19 Inpatients by Race and Ethnicity in a Large Texas Metroplex.

The aim of this study was to assess variations in presentation and outcomes of coronavirus disease 2019 (COVID-19) across race/ethnicity at a large Texas metroplex hospital. A retrospective cohort study was performed. Although COVID-19 patients demonstrated significant socioeconomic disparities, race/ethnicity was not a significant predictor of intensive care unit (ICU) admission (P = 0.067) or case fatality (P = 0.078). Hospital admission varied by month, with incidence among Black/African-American and Hispanic/Latino patients peaking earlier in the pandemic timeline (P < 0.001). Patients reporting Spanish as their primary language were significantly more likely to be admitted to the ICU (odds ratio, 1.75; P = 0.007). COVID-19 patients do not demonstrate significant racial/ethnic disparities in case fatality, suggesting that state-wide disparities in mortality rate are rooted in infection risk rather than hospital course. Variations in admission rates by race/ethnicity across the timeline and increased ICU admission among Spanish-speaking patients demonstrate the need to pursue tailored interventions on both a community and structural level to mitigate further health disparities throughout the pandemic and after.

Reduced muscle mass as predictor of intensive care unit hospitalization in COVID-19 patients.

PurposeTo evaluate if reduced muscle mass, assessed with Computed Tomography (CT), is a predictor of intensive care unit (ICU) hospitalization in COVID-19 patients.MethodsIn this Institution Review Board approved study, we retrospectively evaluated COVID-19 patients treated in our tertiary center from March to November 2020 who underwent an unenhanced chest CT scan within three weeks from hospitalization.We recorded the mean Hounsfield Unit (Hu) value of the right paravertebral muscle at the level of the 12th thoracic vertebra, the hospitalization unit (ICU and COVID-19 wards), clinical symptoms, Barthel Index, and laboratory findings.Logistic regression analysis was applied to assess if muscle loss (Hu<30) is a predictor of ICU admission and outcome.Fisher’s exact and Student’s tests were applied to evaluate if differences between patients with and without muscle loss occurred (p<0.05).ResultsOne-hundred-fifty patients matched the inclusion criteria (46 females; mean age±SD 61.3±15 years-old), 36 treated in ICU. Patients in ICU showed significantly lower Hu values (29±24 vs 39.4±12, p = 0.001). Muscle loss was a predictor of ICU admission (p = 0.004).Patients with muscle loss were significantly older (73.4±10 vs 56.4±14 years), had lower Barthel Index scores (54.4±33 vs 85.1±26), red blood-cell count (3.9±1 vs 4.6±1×1012L−1), and Hb levels (11.5±2 vs 13.2±2g/l) as well as higher white blood-cell count (9.4±7 vs 7.2±4×109L−1), C-reactive protein (71.5±71 vs 44±48U/L), and lactate dehydrogenase levels (335±163 vs 265.8±116U/L) (p<0.05, each).ConclusionsMuscle loss seems to be a predictor of ICU hospitalization in COVID-19 patients and radiologists reporting chest CT at admission should note this finding in their reports.