Clinical Characteristics of Hospitalized Patients With COVID-19 and Their Association With the Progression to Critical Illness and Death: A Single-Center Retrospective Study From Northwestern Mexico.

OBJECTIVE: The objective of this study was to associate the epidemiological and clinical characteristics of patients hospitalized for COVID-19 with the progression to critical illness and death in northwestern Mexico. METHODS: From March to October 2020, we collected the demographic and clinical characteristics of 464 hospitalized patients from northwestern Mexico. RESULTS: Sixty-four percent (295/464) of the patients became critically ill. Age, occupation, steroid and antibiotic use at previous hospitalization, and underlying diseases (hypertension, obesity, and chronic kidney disease) were associated with critical illness or death (p: < 0.05). No symptoms were associated with critical illness. However, the parameters such as the heart rate, respiratory rate, oxygen saturation, and diastolic pressure and the laboratory parameters such as the glucose, creatinine, white line cells, hemoglobin, D-dimer, and C-reactive protein, among others, were associated with critical illness (p: < 0.05). Finally, advanced age, previous hospital treatment, and the presence of one or more underlying diseases were associated with critical illness and death (p: < 0.02). CONCLUSIONS: Several epidemiological (e.g., age and occupation) and clinical factors (e.g., previous treatment, underlying diseases, and vital signs and laboratory parameters) were associated with critical illness and death in patients hospitalized with COVID-19. These data provide us with possible markers to avoid critical illness or death from COVID-19 in our region.

Systematic Review and Meta-Analysis of the Incidence of Chronic Kidney Disease After Pediatric Critical Illness.

OBJECTIVE: Survivors of pediatric critical illnesses are at risk of significant long-term organ sequelae. Chronic kidney disease (CKD) is a complication of critical illness (and ICU interventions) associated with growth impairment, cardiovascular disease, and early death. Our objective was to synthesize the evidence on the incidence of CKD among survivors of pediatric critical illness. DATA SOURCES: MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, and Cochrane Register of Controlled Trials from inception to February 2024. STUDY SELECTION: Observational studies reporting the incidence of de novo CKD among survivors of pediatric critical illness. DATA EXTRACTION: Two reviewers independently extracted data on study design, setting, population, demographics, diagnostic criteria, and outcome. DATA SYNTHESIS: Meta-analysis was used to describe the incidence of CKD among survivors, risk of bias (RoB) assessed using the Joanna Briggs Institute Tool, and strength and reliability of evidence assessed with GRADE (Grading of Recommendations, Assessment, Development, and Evaluations). CKD was quantified as an estimated glomerular filtration rate (eGFR) less than 90 mL/min/1.73 m2 (outcome 1), eGFR less than 60 mL/min/1.73 m2 (outcome 2), and end-stage renal disease (ESRD) as eGFR less than 15 mL/min per 1.73 m2 (outcome 3). Twelve studies (3642 patients) met selection criteria and reported at least one measure of CKD. The median duration of follow-up was 2, 3.6, and 5 years, respectively, for outcomes 1, 2, and 3. For each threshold, the pooled estimate of CKD incidence was 24% (95% CI, 16-32%) for eGFR less than 90, 14% (95% CI, 6-23%) less than 60, and 4% (95% CI, 0-7%) for ESRD. The overall quality assessment indicated a moderate RoB. CONCLUSIONS: Among a heterogenous population of pediatric critical illness survivors, an important minority of survivors developed CKD or ESRD. This study highlights the importance of diagnostic criteria for reporting, a greater focus on postcritical care surveillance and follow-up to identify those with CKD. Further study would facilitate the delineation of high-risk groups and strategies for improved outcomes.

Metformin exposure and the incidence of lactic acidosis in critically ill patients with T2DM: A retrospective cohort study.

OBJECTIVE: The objective of this study was to investigate the correlation between metformin exposure and the incidence of lactic acidosis in critically ill patients. METHODS: The patients with type 2 diabetes mellitus (T2DM) were included from Medical Information Mart for Intensive Care IV database (MIMIC-IV). The primary outcome was the incidence of lactic acidosis. The secondary outcomes were lactate level and in-hospital mortality. Propensity score matching (PSM) method was adopted to reduce bias of the confounders. The multivariate logistic regression was used to explore the correlation between metformin exposure and the incidence of lactic acidosis. Subgroup analysis and sensitivity analysis were used to test the stability of the conclusion. RESULTS: We included 4939 patients. There were 2070 patients in the metformin group, and 2869 patients in the nonmetformin group. The frequency of lactic acidosis was 5.7% (118/2070) in the metformin group and it was 4.3% (122/2869) in the nonmetformin group. There was a statistically significant difference between the two groups (P < 0.05). The lactate level in the metformin group was higher than in the nonmetformin group (2.78 ± 2.23 vs. 2.45 ± 2.24, P < 0.001). After PSM, the frequency of lactic acidosis (6.3% vs. 3.7%, P < 0.001) and lactate level (2.85 ± 2.38 vs. 2.40 ± 2.14, P < 0.001) were significantly higher in the metformin group compared with the nonmetformin group. In multivariate logistic models, the frequency of lactic acidosis was obviously increased in metformin group, and the adjusted odds ratio (OR) of metformin exposure was 1.852 (95% confidence interval (CI) = 1.298-2.643, P < 0.001). The results were consistent with subgroup analysis except for respiratory failure subgroup. Metformin exposure increased lactate level but did not affect the frequency of lactic acidosis in patients of respiratory failure with hypercapnia. However, the in-hospital mortality between metformin and nonmetformin group had no obvious difference (P = 0.215). In sensitivity analysis, metformin exposure showed similar effect as the original cohort. CONCLUSIONS: In critically ill patients with T2DM, metformin exposure elevated the incidence of lactic acidosis except for patients of respiratory failure with hypercapnia, but did not affect the in-hospital mortality.

Is comorbidity alone responsible for changes in health-related quality of life among critical care survivors? A purpose-specific review.

BACKGROUND: Health-related quality of life (HRQoL) is one of the most important outcome variables for assessing the effectiveness of intensive care, together with mortality and survival, where comorbidity is suggested to have high impact. However, studies are lacking that examine to what extent HRQoL is affected after a general ICU period, beyond that of the effects that may be claimed to be due to comorbidities. DESIGN: Purpose-specific literature review including literature searches in PubMed, Cinahl, Scopus, and Cochrane library between 2010 and 2021. MEASUREMENTS AND RESULTS: This Purpose-specific, i.e., task focused review examines HRQoL (assessed by either SF-36 or EQ-5D, > 30 days after leaving the hospital) in adult patients (≥ 18 years) having an ICU length of stay > 24 h. Further, the HRQoL comparisons were adjusted for age or comorbidity. A total of 11 publications were found. A majority comprised observational, prospective cohort studies, except three that were either case-control, cross-sectional comparison, or retrospective cohort studies. A total of 18,566 critically ill patients were included, and the response rate ranged from 16 to 94%. In all studies, a recurrent relevant finding was that HRQoL after ICU care was affected by pre-ICU comorbidities. In three studies (n = 3), which included a comorbidity adjusted control group, there were no effect of the critical care period itself on the registered HRQoL after the critical care period. CONCLUSION: Health-Related Quality of Life (HRQoL) in former ICU patients appears to be primarily influenced by comorbidity. A notable limitation in this field of research is the high heterogeneity observed in the studies reviewed, particularly in terms of the HRQoL measurement tool employed, the duration of follow-up, the methodology for comorbidity assessment, and the adjustments for age and sex. Despite these variations and the limited number of studies in the review, the findings suggest a minimal HRQoL impact beyond the effects of comorbidity. Given the significant dearth of comprehensive studies in this domain, there is an escalating call for more thorough and detailed research endeavours.

Incidence and Risk Factors for Medical Device-Related Pressure Injury in Hemodynamically Unstable Intensive Care Unit Patients: A Prospective Cohort Study.

PURPOSE: The purpose of this study was to determine the incidence and identify potential risk factors for medical device-related pressure injury (MDRPI) in critically ill and hemodynamically unstable adults (patients classified class III or IV on the Therapeutic Intervention Scoring System-28; TISS-28). DESIGN: Prospective cohort study. SUBJECTS AND SETTING: The target population was critically ill adults who were using one or more medical devices and categorized as class III or IV on the TISS-28. The study sample comprised 77 participants followed daily until discharge, death, transfer, or lesion development. Data were collected from January to March 2020. The study setting was an intensive care unit with 40 beds in a large hospital in a municipality in the state of Minas Gerais, Brazil. METHODS: Sociodemographic and pertinent clinical data, pressure injury (PI) risk assessed using the Braden Scale for Pressure Sore Risk, and head-to-toe skin inspections were completed. The incidence rate of MDRPIs was calculated, and survival analyses were completed via the Kaplan-Meier method and Cox regression model. RESULTS: Forty-nine of 77 participants developed an MDRPI, reflecting an incidence rate of 63.6%. Collectively, 71 MDRPIs occurred in these 49 participants. Univariate analysis indicated significant associations between MDRPI occurrences and level of consciousness ( P = < .001), use of tube holder for ventilation devices ( P = .013), nasal cannula ( P = .034), nasogastric cannula ( P = .034), presence of edema ( P = .001), infection ( P = .007), higher TISS score ( P = .047), and greater number of medical devices ( P = .022). Survival analysis indicated that a high or very high-risk score on the Braden Scale for Pressure Sore Risk ( P = .043) and edema ( P = .030) are risk factors for MDRPI occurrences in this vulnerable population. CONCLUSIONS: The incidence rate of MDRPIs was 63.6%. The categories with the highest high or very high risk scores on the Braden Scale for Risk of Pressure Pain and Edema emerged as risk factors for MDRPI in this critically ill and vulnerable population.

Multidrug-resistant pathogens and ventilator-associated pneumonia in critically ill COVID-19 and non-COVID-19 patients: a prospective observational monocentric comparative study.

BACKGROUND: The COVID-19 pandemic has increased the incidence of ventilator-associated pneumonia (VAP) among critically ill patients. However, a comparison of VAP incidence in COVID-19 and non-COVID-19 cohorts, particularly in a context with a high prevalence of multidrug-resistant (MDR) organisms, is lacking. MATERIAL AND METHODS: We conducted a single-center, mixed prospective and retrospective cohort study comparing COVID-19 patients admitted to the intensive care unit (ICU) of the "Città della Salute e della Scienza" University Hospital in Turin, Italy, between March 2020 and December 2021 (COVID-19 group), with a historical cohort of ICU patients admitted between June 2016 and March 2018 (NON-COVID-19 group). The primary objective was to define the incidence of VAP in both cohorts. Secondary objectives were to evaluate the microbial cause, resistance patters, risk factors and impact on 28 days, ICU and in-hospital mortality, duration of ICU stay, and duration of hospitalization). RESULTS: We found a significantly higher incidence of VAP (51.9% - n = 125) among the 241 COVID-19 patients compared to that observed (31.2% - n = 78) among the 252 NON-COVID-19 patients. The median SOFA score was significantly lower in the COVID-19 group (9, Interquartile range, IQR: 7-11 vs. 10, IQR: 8-13, p < 0.001). The COVID-19 group had a higher prevalence of Gram-positive bacteria-related VAP (30% vs. 9%, p < 0.001), but no significant difference was observed in the prevalence of difficult-to-treat (DTR) or MDR bacteria. ICU and in-hospital mortality in the COVID-19 and NON-COVID-19 groups were 71% and 74%, vs. 33% and 43%, respectively. The presence of COVID-19 was significantly associated with an increased risk of 28-day all-cause hospital mortality (Hazard ratio, HR: 7.95, 95% Confidence Intervals, 95% CI: 3.10-20.36, p < 0.001). Tracheostomy and a shorter duration of mechanical ventilation were protective against 28-day mortality, while dialysis and a high SOFA score were associated with a higher risk of 28-day mortality. CONCLUSION: COVID-19 patients with VAP appear to have a significantly higher ICU and in-hospital mortality risk regardless of the presence of MDR and DTR pathogens. Tracheostomy and a shorter duration of mechanical ventilation appear to be associated with better outcomes.

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies.

BACKGROUND: Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the "last line of defense" for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies. OBJECTIVE: This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves. METHODS: A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients. RESULTS: Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic. CONCLUSION: The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

Patient characteristics associated with posttraumatic stress symptoms in intensive care unit survivors during a one-year follow-up: A multicenter study.

BACKGROUND: Intensive care unit (ICU) patients are at risk of suffering from posttraumatic stress symptoms (PTSS) after ICU survival. OBJECTIVES: To describe the prevalence of high levels of PTSS the first year after ICU admission. Further, to identify specific combinations of patient characteristics (latent classes based on pre-ICU data, demographics, and clinical characteristics), and to investigate possible associations among these classes and PTSS at 3, 6, and 12 months after ICU admission. METHODS: Self-reported PTSS were measured with Impact of Event Scale-Revised (IES-R). PTSS and possible predictive factors (pre-ICU data, demographics, and clinical characteristics) were analyzed using descriptive statistics, latent class analysis, and linear mixed model for repeated measures. RESULTS: High PTSS levels (IES-R ≥ 33) were reported by 14.9 % (95 % confidence interval [CI] [10.0; 21.1]), 16.7 % (95 % CI [11.5; 23.1]), and 18.4 % (95 % CI [12.9; 25.0]) of patients (sample 1, n = 174) at 3, 6, and 12 months, respectively. Three latent classes were identified (sample 2, n = 417). PTSS were significantly associated with class 2 (male with longer hospital stay) at 6 months and class 3 (age≥70, lower level of education, higher Simplified Acute Physiology Score, being mechanically ventilated) at all three measurement times. CONCLUSIONS: The prevalence of high levels of PTSS is the greatest 12 months after ICU admission. Health professionals can use this information to be aware of specific groups of ICU patients reporting PTSS during the first year and follow up on these.

The impact of different co-morbidities on clinical outcomes and resource utilization in critically ill burn and surgical patients: A population-based analysis of social determinants of health.

BACKGROUND: This study aims to establish the significance of social determinants of health and prevalent co-morbidities on multiple indicators for quality of care in patients admitted to the Burn and Surgical Intensive Care Unit (ICU). METHODS: We performed a retrospective analysis of population group data for patients admitted at the Burn and Surgical ICU from January 1, 2016, to November 18, 2019. The primary outcomes were length of hospital stay (LOS), mortality, 30-day readmission, and hospital charges. Pearson's chi-square test for categorical variables and t-test for continuous variables were used to compare population health groups. RESULTS: We analyzed a total of 487 burn and 510 surgical patients. When comparing ICU patients, we observed significantly higher mean hospital charges and length of stay (LOS) in BICU v. SICU patients with a history of mental health ($93,259.40 v. $50,503.36, p = 0.013 and 16.28 v. 9.16 days, p = 0.0085), end-stage-renal-disease (ESRD) ($653,871.05 v. $75,746.35, p = 0.0047 and 96.15 v. 17.53 days, p = 0.0104), sepsis ($267,979.60 v. $99,154.41, p = <0.001 and 39.1 v. 18.42 days, p = 0.0043), and venous thromboembolism (VTE) ($757,740.50 v. $117,816.40, p = <0.001 and 93.11 v. 20.21 days, p = 0.002). Also, higher mortality was observed in burn patients with ESRD, ST-Elevation Myocardial Infarction (STEMI), sepsis, VTE, and diabetes mellitus. 30-day-readmissions were greater among burn patients with a history of mental health, drug dependence, heart failure, and diabetes mellitus. CONCLUSIONS: Our study provides new insights into the variability of outcomes between burn patients treated in different critical care settings, underlining the influence of comorbidities on these outcomes. By comparing burn patients in the BICU with those in the SICU, we aim to highlight how differences in patient backgrounds, including the quality of care received, contribute to these outcomes. This comparison underscores the need for tailored healthcare strategies that consider the unique challenges faced by each patient group, aiming to mitigate disparities in health outcomes and healthcare spending. Further research to develop relevant and timely interventions that can improve these outcomes.

Association between body mass index and delirium incidence in critically ill patients: a retrospective cohort study based on the MIMIC-IV Database.

OBJECTIVES: Delirium is a form of brain dysfunction with high incidence and is associated with many negative outcomes in the intensive care unit. However, few studies have been large enough to reliably examine the associations between body mass index (BMI) and delirium, especially in critically ill patients. The objective of this study was to investigate the association between BMI and delirium incidence in critically ill patients. DESIGN: A retrospective cohort study. SETTING: Data were collected from the Medical Information Mart for Intensive Care-IV V2.0 Database consisting of critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston. PARTICIPANTS: A total of 20 193 patients with BMI and delirium records were enrolled in this study and were divided into six groups. PRIMARY OUTCOME MEASURE: Delirium incidence. RESULTS: Generalised linear models and restricted cubic spline analysis were used to estimate the associations between BMI and delirium incidence. A total of 30.81% of the patients (6222 of 20 193) developed delirium in the total cohort. Compared with those in the healthy weight group, the patients in the different groups (underweight, overweight, obesity grade 1, obesity grade 2, obesity grade 3) had different relative risks (RRs): RR=1.10, 95% CI=1.02 to 1.19, p=0.011; RR=0.93, 95% CI=0.88 to 0.97, p=0.003; RR=0.88, 95% CI=0.83 to 0.94, p<0.001; RR=0.94, 95% CI=0.86 to 1.03, p=0.193; RR=1.14, 95% CI=1.03 to 1.25, p=0.010, respectively. For patients with or without adjustment variables, there was an obvious U-shaped relationship between BMI as a continuous variable and delirium incidence. CONCLUSION: BMI was associated with the incidence of delirium. Our results suggested that a BMI higher or lower than obesity grade 1 rather than the healthy weight in critically ill patients increases the risk of delirium incidence.

Prognostic Value of Leukocyte-Based Risk Model for Acute Kidney Injury Prediction in Critically Ill Acute Exacerbation of Chronic Obstructive Pulmonary Disease Patients.

Purpose: Acute kidney injury (AKI) is a common complication of acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and inflammation is the potential link between AKI and AECOPD. However, little is known about the incidence and risk stratification of AKI in critically ill AECOPD patients. In this study, we aimed to establish risk model based on white blood cell (WBC)-related indicators to predict AKI in critically ill AECOPD patients. Material and Methods: For the training cohort, data were taken from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database, and for the validation cohort, data were taken from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The study employed logistic regression analysis to identify the major predictors of WBC-related biomarkers on AKI prediction. Subsequently, a risk model was developed by multivariate logistic regression, utilizing the identified significant indicators. Results: Finally, 3551 patients were enrolled in training cohort, 926 patients were enrolled in validation cohort. AKI occurred in 1206 (33.4%) patients in training cohort and 521 (56.3%) patients in validation cohort. According to the multivariate logistic regression analysis, four WBC-related indicators were finally included in the novel risk model, and the risk model had a relatively good accuracy for AKI in the training set (C-index, 0.764, 95% CI 0.749-0.780) as well as in the validation set (C-index, 0.738, 95% CI: 0.706-0.770). Even after accounting for other models, the critically ill AECOPD patients in the high-risk group (risk score > 3.44) still showed an increased risk of AKI (odds ratio: 4.74, 95% CI: 4.07-5.54) compared to those in low-risk group (risk score ≤ 3.44). Moreover, the risk model showed outstanding calibration capability as well as therapeutic usefulness in both groups for AKI and ICU mortality and in-hospital mortality of critical ill AECOPD patients. Conclusion: The novel risk model showed good AKI prediction performance. This risk model has certain reference value for the risk stratification of AECOPD complicated with AKI in clinically.

Coexistence of a fluid responsive state and venous congestion signals in critically ill patients: a multicenter observational proof-of-concept study.

BACKGROUND: Current recommendations support guiding fluid resuscitation through the assessment of fluid responsiveness. Recently, the concept of fluid tolerance and the prevention of venous congestion (VC) have emerged as relevant aspects to be considered to avoid potentially deleterious side effects of fluid resuscitation. However, there is paucity of data on the relationship of fluid responsiveness and VC. This study aims to compare the prevalence of venous congestion in fluid responsive and fluid unresponsive critically ill patients after intensive care (ICU) admission. METHODS: Multicenter, prospective cross-sectional observational study conducted in three medical-surgical ICUs in Chile. Consecutive mechanically ventilated patients that required vasopressors and admitted < 24 h to ICU were included between November 2022 and June 2023. Patients were assessed simultaneously for fluid responsiveness and VC at a single timepoint. Fluid responsiveness status, VC signals such as central venous pressure, estimation of left ventricular filling pressures, lung, and abdominal ultrasound congestion indexes and relevant clinical data were collected. RESULTS: Ninety patients were included. Median age was 63 [45-71] years old, and median SOFA score was 9 [7-11]. Thirty-eight percent of the patients were fluid responsive (FR+), while 62% were fluid unresponsive (FR-). The most prevalent diagnosis was sepsis (41%) followed by respiratory failure (22%). The prevalence of at least one VC signal was not significantly different between FR+ and FR- groups (53% vs. 57%, p = 0.69), as well as the proportion of patients with 2 or 3 VC signals (15% vs. 21%, p = 0.4). We found no association between fluid balance, CRT status, or diagnostic group and the presence of VC signals. CONCLUSIONS: Venous congestion signals were prevalent in both fluid responsive and unresponsive critically ill patients. The presence of venous congestion was not associated with fluid balance or diagnostic group. Further studies should assess the clinical relevance of these results and their potential impact on resuscitation and monitoring practices.

[Predictive value of von Willebrand factor for venous thromboembolism in critically ill patients based on propensity score matching].

OBJECTIVE: To analyze the predictive value of von Willebrand factor (vWF) for venous thromboembolism (VTE) of patients in intensive care unit (ICU) by using propensity score matching (PSM). METHODS: Patients admitted to ICU of the Second Affiliated Hospital of Kunming Medical University from December 2020 to June 2022 who stayed in ICU for ≥72 hours and underwent daily bedside vascular ultrasound screening were included. Baseline data such as age, gender, primary disease, and chronic comorbidities were collected. Coagulation indexes before admission to ICU and 24 hours and 48 hours after ICU admission were collected, including prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), international normalized ratio (INR), fibrinogen (Fib), fibrin monomer (FM), vWF, D-dimer, antithrombin III (ATIII), etc. Patients were divided into VTE group and non-VTE group according to whether they had VTE or not [diagnosis of VTE: patients underwent daily ultrasound screening of bedside blood vessels (both upper and lower limbs, visceral veins), and those suspected of having thrombosis were confirmed by ultrasonographer or pulmonary angiography]. Using PSM analysis method, the VTE group was used as the benchmark to conduct 1 : 1 matching of age, whether there was malignant tumor, whether there was infection, whether there was diabetes, and coagulation indicators before admission to ICU. Finally, the cases with balanced covariates between the two groups were obtained. The risk factors of VTE were analyzed by multivariate Logistic regression analysis. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of vWF in the occurrence of VTE in critically ill patients. RESULTS: A total of 120 patients were enrolled, of which 18 (15.0%) were diagnosed with VTE within 72 hours after admission to ICU, and 102 (85.0%) were not found to have thrombus in ICU. Before PSM, there were significant differences in age, gender, proportion of malignant tumor and infection, and coagulation indexes between VTE group and non-VTE group. After PSM, 14 pairs were successfully matched, and the unbalanced covariables between the two groups reached equilibrium. Multivariate Logistic regression analysis showed that vWF was an independent risk factor for VTE at 48 hours after ICU admission in critically ill patients [odds ratio (OR) = 1.165, 95% confidence interval (95%CI) was 1.000-1.025, P = 0.004]. ROC curve analysis showed that the area under the ROC curve (AUC) of vWF at 48 hours after ICU admission for predicting VTE was 0.782, 95%CI was 0.618-0.945, P = 0.007. When the optimal cut-off value was 312.12%, the sensitivity was 67.7% and the specificity was 93.0. CONCLUSIONS: Dynamic monitoring of vWF is helpful to predict the occurrence of VTE in ICU patients, and vWF at 48 hours after ICU admission has certain value in predicting the occurrence of VTE.

SNARE-ing the Reason for Post-Cardiac Surgery Critical Illness-Related Corticosteroid Insufficiency.

Critical illness-related corticosteroid insufficiency (CIRCI) can cause hemodynamic instability in neonates after congenital heart surgery with manifestations that increase morbidity and potential mortality. We retrospectively reviewed neonates who underwent cardiac surgery between August 2018 and July 2020 at a freestanding children's hospital, had next-generation sequencing performed, and had their cortisol levels drawn as standard clinical care after cardiac surgery. The groups were defined as CIRCI (with a cortisol level ≤ 4.5 mcg/dL) and non-CIRCI (level > 4.5 mcg/dL). The CIRCI group (n = 8) had a 100% incidence of heterozygous gene mutation on STX1A with splicing or loss of function, and this mutation was not found in the non-CIRCI group (n = 8). Additional gene mutations were found in the CIRCI group on RAB6A, ABCA3, SIDT2, and LILRB3, with no incidence in the non-CIRCI group. Three additional mutations were found across the CIRCI group in INPPL1 and FAM189A2 (both splicing and missense), with 12-25% of patients in the non-CIRCI group also displaying these mutations. Novel genetic abnormalities were seen in neonates with symptoms of CIRCI with potential cardiac implications from a gene mutation for STX1A. Compounding effects of additional gene mutations need to be confirmed and explored for potential predisposition to hemodynamic instability during times of stress.

Risk factors for prolonged mechanical ventilation in critically ill patients with influenza-related acute respiratory distress syndrome.

BACKGROUND: Patients with influenza-related acute respiratory distress syndrome (ARDS) are critically ill and require mechanical ventilation (MV) support. Prolonged mechanical ventilation (PMV) is often seen in these cases and the optimal management strategy is not established. This study aimed to investigate risk factors for PMV and factors related to weaning failure in these patients. METHODS: This retrospective cohort study was conducted by eight medical centers in Taiwan. All patients in the intensive care unit with virology-proven influenza-related ARDS requiring invasive MV from January 1 to March 31, 2016, were included. Demographic data, critical illness data and clinical outcomes were collected and analyzed. PMV is defined as mechanical ventilation use for more than 21 days. RESULTS: There were 263 patients with influenza-related ARDS requiring invasive MV enrolled during the study period. Seventy-eight patients had PMV. The final weaning rate was 68.8% during 60 days of observation. The mortality rate in PMV group was 39.7%. Risk factors for PMV were body mass index (BMI) > 25 (kg/m2) [odds ratio (OR) 2.087; 95% confidence interval (CI) 1.006-4.329], extracorporeal membrane oxygenation (ECMO) use (OR 6.181; 95% CI 2.338-16.336), combined bacterial pneumonia (OR 4.115; 95% CI 2.002-8.456) and neuromuscular blockade use over 48 h (OR 2.8; 95% CI 1.334-5.879). In addition, risk factors for weaning failure in PMV patients were ECMO (OR 5.05; 95% CI 1.75-14.58) use and bacteremia (OR 3.91; 95% CI 1.20-12.69). CONCLUSIONS: Patients with influenza-related ARDS and PMV have a high mortality rate. Risk factors for PMV include BMI > 25, ECMO use, combined bacterial pneumonia and neuromuscular blockade use over 48 h. In addition, ECMO use and bacteremia predict unsuccessful weaning in PMV patients.

Family burden after critical illness: the forgotten caregivers.

OBJECTIVE: To determine the incidence of primary caregiver burden in a cohort of family members of critically ill patients admitted to ICU and to identify risk factors related to its development in both the patient and the family member. DESIGN: Prospective observational cohort study was conducted for 24 months. SETTING: Hospital Universitario Clínico San Cecilio, Granada. PATIENTS: The sample was the primary caregivers of all patients with risk factors for development of PICS (Post-Intensive Care Syndrome). INTERVENTIONS: The follow-up protocol consisted of evaluation 3 months after discharge from the ICU in a specific consultation. MAIN VARIABLES OF INTEREST: The scales used in patients were Barthel, SF-12, HADS, Pfeiffer, IES-6 and in relatives the Apgar and Zarit. RESULTS: A total of 93 patients and caregivers were included in the follow-up. 15 relatives did not complete the follow-up questionnaires and were excluded from the study. The incidence of PICS-F (Family Post Intensive Care Syndrome) defined by the presence of primary caregiver burden in our cohort of patients is 34.6% (n=27), 95% CI 25.0-45.7. The risk factors for the development of caregiver burden are the presence of physical impairment, anxiety or post-traumatic stress in the patient, with no relationship found with the characteristics studied in the family member. CONCLUSIONS: One out of 3 relatives of patients with risk factors for the development of PICS presents at 3 months caregiver burden. This is related to factors dependent on the patient's state of health.