Mortality In Intensive Care Patients Research Articles

Evaluation of the relationship of intra-abdominal hypertension with respiratory mechanics, hemodynamic parameters and mortality in intensive care patients

Evaluation of the relationship of intra-abdominal hypertension with respiratory mechanics, hemodynamic parameters and mortality in intensive care patients

Association Between the Red Blood Cell Distribution Width and 30-Day Mortality in Intensive Care Patients Undergoing Cardiac Surgery: A Retrospective Observational Study Based on the Medical Information Mart for Intensive Care-IV Database.

Millions of patients undergo cardiac surgery each year. The red blood cell distribution width (RDW) could help predict the prognosis of patients who undergo percutaneous coronary intervention or coronary artery bypass surgery. We investigated whether the RDW has robust predictive value for the 30-day mortality among patients in an intensive care unit (ICU) after undergoing cardiac surgery. Using the Medical Information Mart for Intensive Care-IV Database, we retrieved data for 11,634 patients who underwent cardiac surgery in an ICU. We performed multivariate Cox regression analysis to model the association between the RDW and 30-day mortality and plotted Kaplan-Meier curves. Subgroup analyses were stratified using relevant covariates. Receiver operating characteristic (ROC) curves were used to determine the predictive value of the RDWs. The total 30-day mortality rate was 4.2% (485/11,502). The elevated-RDW group had a higher 30-day mortality rate than the normal-RDW group (P<0.001). The robustness of our data analysis was confirmed by performing subgroup analyses. Each unit increase in the RDW was associated with a 17% increase in 30-day mortality when the RDW was used as a continuous variable (adjusted hazard ratio=1.17, 95% confidence interval, 1.10-1.25). Our ROC results showed the predictive value of the RDW. An elevated RDW was associated with a higher 30-day mortality in patients after undergoing cardiac surgery in an ICU setting. The RDW can serve as an efficient and accessible method for predicting the mortality of patients in ICUs following cardiac surgery.

Association between glycemic variability and short-term mortality in patients with acute kidney injury: a retrospective cohort study of the MIMIC-IV database

Acute kidney injury (AKI) represents a significant challenge to global public health problem and is associated with poor outcomes. There is still considerable debate about the effect of mean blood glucose (MBG) and coefficient of variation (CV) of blood glucose on the short-term mortality of AKI patients. This retrospective cohort study aimed to explore the association between glycemic variability and short-term mortality in patients with AKI. Data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed, including 6,777 adult AKI patients. MBG and CV on the first day of ICU admission were calculated to represent the overall glycemic status and variability during the ICU stay in AKI patients. The primary outcome indicator was ICU 30-day mortality of AKI patients. Multivariate Cox regression analysis and smoothed curve fitting were used to assess the relationship between blood glucose levels and mortality. Eventually, the ICU 30-day mortality rate of AKI patients was 23.5%. The increased MBG and CV were significantly correlated with ICU 30-day mortality (hazards ratio (HR) = 1.20, 95% confidence interval (CI) 1.14–1.27; HR = 1.08, 95% CI 1.03–1.13). The smoothed curve fitting showed a U-shaped relationship between MBG on the first day of ICU admission and ICU 30-day mortality (inflection point = 111.3 mg/dl), while CV had a linear relationship with 30-day ICU mortality. Thus, we conclude that MBG and CV were significantly associated with short-term mortality in intensive care patients with AKI. Tighter glycemic control may be an effective measure to improve the prognosis of patients with AKI.

Association between albumin-corrected anion gap and in-hospital mortality of intensive care patients with trauma: A retrospective study based on MIMIC-Ⅲ and Ⅳ databases.

To investigate the correlation between albumin-corrected anion gap(ACAG) within the first 24 hours of admission and in-hospital mortality in trauma patients in intensive care unit(ICU). We utilized the MIMIC-Ⅲ and MIMIC-Ⅳ databases to examine trauma patients admitted to the ICU. The relationship between ACAG and in-hospital mortality in trauma patients was analyzed using Receiver Operating Characteristic(ROC) curve, Kaplan-Meier (K-M) survival curve, and Cox regression model. Propensity score matching (PSM) and subgroup analysis were conducted to enhance stability and reliability of the findings. Mortality at 30-day and 90-day served as secondary outcomes. The study enrolled a total of 1038 patients. The AUC for ACAG (0.701, 95%CI: 0.652-0.749) was notably higher than that for anion gap and albumin. The Log-rank test revealed that the optimal cut-off point of ACAG for predicting in-hospital mortality was determined to be 20.375mmol/L. The multivariate Cox regression analysis demonstrated an independent association between high ACAG level and a higher risk of in-hospital mortality (HR = 3.128, 95% CI: 1.615-6.059). After PSM analysis, a matched cohort consisting of 291 subjects was obtained. We found no signifcant interaction in most stratas. Finally, The in-hospital, 30-day, and 90-day survival rates in the high ACAG group exhibited a statistically decrease compared to those in the low ACAG group both pre- and post-matching. The elevated level of ACAG was found to be independently associated with increased in-hospital mortality among trauma patients in the ICU.

SIMPLIFIED IMMUNE-DYSREGULATION INDEX: A NOVEL MARKER PREDICTS 28-DAY MORTALITY OF INTENSIVE CARE PATIENTS WITH SEPSIS.

Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. There is currently no simple immune-imbalance-driven indicator for patients with sepsis. Methods: This study was conducted in Peking Union Medical College Hospital. Patients with sepsis were identified according to Sepsis 3.0 after reviewing patient data from May 2018 through October 2022. Least absolute shrinkage and selection operator logistic regression was used for features selection. Receiver operating characteristic curves for 28-day mortality were used to compare the predictive performance of level of interleukin 6 (IL-6) and lymphocyte count (LY#) with that of the combined ratio, namely, the IL-6/LY# ratio. A Cox hazard model was also used to test the predictive performance of IL-6/LY# versus several other measurements. The dynamic trend of IL-6/LY# based on day 1 IL-6/LY# level was analyzed. Results: The mortality rate was 24.5% (220/898) in the study cohort. The LY#, IL-6 level, blood platelet count, Sequential Organ Failure Assessment score, Acute Physiology and Chronic Health Evaluation II score, heart rate, age and Fi o2 level were identified as key factors for predicting 28-day mortality. IL-6/LY# was identified as a core indicator according to Least absolute shrinkage and selection operator logistic regression analysis. IL-6/LY# was significantly higher in nonsurvivors than in survivors (348 [154.6-1371.7] vs. 42.3 [15.4-117.1]). IL-6/LY# yielded a higher area under the curve (0.852 [95% CI = 0.820-0.879]) than the level of IL-6 (0.776 [95% CI = 0.738-0.809]) and LY# (0.719 [95% CI = 0.677-0.755]) separately. Survival analysis of mortality risk versus the IL-6/LY# ratio suggested that IL-6/LY# was significantly more predictive of patient risk than the Sequential Organ Failure Assessment score or the other factors ( P = 1.5 × 10 -33 ). In trend analysis, as the trend of D1-D3-D7 IL-6/LY# decreases, the morality rate is lower than increase or fluctuate group (42.1% vs. 58.3%, 37.9% vs. 43.8%, 37.5% vs. 38.5% in high, moderate, and low D1 IL-6/LY# group separately). Conclusion: IL-6/LY# examined on first day in intensive care unit can be used as an immune-imbalance alert to identify sepsis patients with higher risk of 28-day mortality. Decreasing trend of IL-6/LY# suggests a lower 28-day mortality rate of sepsis patients.

Relationship between anion gap and in-hospital mortality in intensive care patients with liver failure: a retrospective propensity score matching analysis

Relationship between anion gap and in-hospital mortality in intensive care patients with liver failure: a retrospective propensity score matching analysis

Impaired Whole-Blood Fibrinolysis is a Predictor of Mortality in Intensive Care Patients.

Background Altered fibrinolysis is considered to play a crucial role in the development of coagulopathy in sepsis. However, routine laboratory tests for fibrinolysis are currently very limited, and the impact of fibrinolytic capacity on clinical outcome is poorly investigated. Objectives To assess whole-blood fibrinolysis in patients admitted to the intensive care unit (ICU) and compare fibrinolysis in sepsis patients with nonsepsis patients. Further, to investigate associations between fibrinolytic capacity and 30-day mortality and venous thromboembolism (VTE). Methods This study was designed as a prospective cohort study. Adult ICU patients were included at the Aarhus University Hospital, Denmark. All patients had a blood sample obtained the morning after admission. A modified thromboelastometry (ROTEM®) analysis with tissue plasminogen activator (ROTEM®-tPA) was used to assess fibrinolysis. The primary endpoint was difference in ROTEM®-tPA lysis time between sepsis patients and nonsepsis patients. Results ROTEM®-tPA revealed fibrinolytic impairment in sepsis patients ( n = 30) compared with nonsepsis ICU controls ( n = 129), with longer lysis time (median [interquartile range] 3,600 [3,352-3,600] vs. 3,374 seconds [2,175-3,600], p < 0.01), lower maximum lysis (23 [8-90] vs. 94% [14-100], p = 0.02), and lower fibrinolysis speed (0.41 [0.0-1.4] vs. 1.6 mm/min [0.1-2.7], p = 0.01). In the composite ICU population, 61% (97/159) demonstrated prolonged lysis time indicating impaired fibrinolytic capacity. These patients had higher 30-day mortality (adjusted odds ratio [OR]: 2.26 [0.83-6.69]) and VTE risk (OR: 3.84 [0.87-17.8]) than patients with normal lysis time. Conclusion Sepsis patients showed impaired fibrinolysis measured with ROTEM®-tPA compared with nonsepsis patients and ROTEM®-tPA lysis time was associated with 30-day mortality and VTE in the entire ICU cohort.

The Effect of Urea/Albumin Ratio on Mortality in the Geriatric Patients Admitted in the Intensive Care Unit.

To analyse the effect of urea albumin ratio on mortality in intensive care patients aged over 65 years. An observational study. Place and Duration of the Study: Health Sciences University, Ankara Training and Research Hospital, Emergency Critical Intensive Care Unit, from October 2021 to March 2022. Patients over 65 years of age admitted in the intensive care unit were included in this study, and the laboratory and clinical data were recorded within the first 24 hours after the admission of the patient. The urea : albumin ratio was calculated using serum urea and albumin concentrations. All the patients were monitored during intensive care admission and mortality rates were recorded; additionally 1-month mortalities of the patients were also recorded. The data of the survivors and non-survivors were compared. There were 362 patients with mean age of 79.00±8.40 years; 53.9% were females. The median urea : albumin ratio was found to be 22.8 mg/gr. The area under curve, sensitivity and specificity values were found to be 0.631, 68%, and 56% at a cut-off value of 22.1 mg/gr for urea : albumin ratio, respectively. In addition, urea : albumin ratio >22.1mg/gr was found to be significant in univariate regression analysis (p<0.001) and multivariate regression analysis (p<0.002) for the predictive value of 1-month mortality. Urea : albumin ratio is a simple and potentially useful prognostic factor in the geriatric patient population admitted in the intensive care unit. Geriatrics intensive care unit, Urea:albumin ratio, Mortality (MeSH database).

A novel score for predicting 1-year mortality of intensive care patients.

We aimed to develop a simple scoring table for predicting probability of death within 1-year after admission to an intensive care unit. We analysed data on emergency admissions from the nationwide Finnish intensive care quality registry. We included first admissions of adult patients with data available on 1-year vital status (dead or alive) and all five variables included in a premorbid functional status score, which is the number of activities the person can manage independently of the following five: get out of bed, move indoors, dress, climb stairs and walk 400 m. We analysed data on patient characteristics and admission-associated factors from 2012 to 2014 to find predictors of 1-year mortality and to develop a score for predicting probability of death. We tested the performance of this score in data from 2015. We assessed the 1-year functional status score of survivors with data available. Out of 25,261 patients, 20,628 (81.7%) patients were able to perform all five functional activities independently prior to the intensive care unit admission. At 1-year post admission, 19,625 (77.7%) patients were alive. 1-year functional status score was known for 11,011 patients and 8970 (81.5%) patients achieved functional status score 5, managing all five activities independently. The score based on age, sex, preceding functional status, type of intensive care unit admission, severity of acute illness and the most significant diagnoses predicted 1-year mortality with an area under the receiver operating characteristic curve 0.78 (95% CI, 0.76-0.79). The calibration of our prediction model was good, with calibration intercept -0.01 (-0.07 to 0.05) and calibration slope 0.96 (0.90 to 1.02). Our score based on data available at intensive care unit admission predicted 1-year mortality with fairly good discrimination. Most survivors achieved good functional recovery.

Fungal Infections Are Not Associated with Increased Mortality in COVID-19 Patients Admitted to Intensive Care Unit (ICU).

Fungal infection is a cause of increased morbidity and mortality in intensive care patients. Critically unwell patients are at increased risk of developing invasive fungal infections. COVID-19 patients in the intensive care unit (ICU) may be at a particularly high risk. The primary aim of this study was to establish the incidence of secondary fungal infections in patients admitted to the ICU with COVID-19. Secondary aims were to investigate factors that may contribute to an increased risk of fungal infections and to calculate the mortality between fungal and nonfungal groups. We undertook a retrospective observational study in a tertiary ICU in Wales, United Kingdom. 174 patients admitted with COVID-19 infection from March 2020 until May 2021 were included. Data were collected through a retrospective review of patient's clinical notes and microbiology investigation results obtained from the online clinical portal. 81/174 (47%) COVID-19 patients developed fungal infections, 93% of which were Candida species, including Candida albicans (88%), and 6% had an Aspergillus infection. Age and smoking history did not appear to be contributing factors. The nonfungal group had a significantly higher body mass index (33 ± 8 vs. 31 ± 7, p=0.01). The ICU length of stay (23 (1-116) vs. 8 (1-60), p < 0.001), hospital length of stay (30 (3-183) vs. 15 (1-174) ± 7, p < 0.001), steroid days (10 (1-116) vs. 4 (0-28), p=0.02), and ventilation days (18 (0-120) vs. 2 (0-55), p < 0.001) were significantly higher in the fungal group. The mortality rate in both groups was similar (51% vs. 52%). The Kaplan-Meier survival analysis showed that the fungal group survived more than the nonfungal group (log rank (Mantel-Cox), p < 0.001). Secondary fungal infections are common in COVID-19 patients admitted to the ICU. Longer treatment with corticosteroids, increased length of hospital and ICU stay, and greater length of mechanical ventilation significantly increase the risk of fungal infections. Fungal infection, however, was not associated with an increase in mortality.

Potential value of red blood cell distribution width in predicting in-hospital mortality in intensive care US population with acute pancreatitis: a propensity score matching analysis

The association between red blood cell distribution width (RDW) and in-hospital mortality in intensive care patients with acute pancreatitis (AP) is inconclusive. We extracted the baseline data, Bedside Index for Severity in Acute Pancreatitis (BISAP) score, Sequential Organ Failure Assessment (SOFA) score, and in-hospital prognosis of intensive care patients with AP from the Medical Information Mart for Intensive Care IV database. Performing binary logistic regression analysis to determine whether RDW is an independent risk factor for in-hospital mortality. By drawing receiver operating characteristic (ROC) curves and comparing the areas under the ROC curves (AUC) to determine the predictive value of RDW for in-hospital mortality, and by conducting survival analysis to evaluate the impact of RDW on survival time in hospital. Before and after the propensity score matching (PSM) analysis, RDW was always a risk factor for in-hospital mortality in patients with AP. The AUC of RDW was comparable to BISAP, while the AUCs of combining RDW and BISAP or SOFA were greater than that of BISAP or SOFA alone. The median survival time of the high-RDW group (RDW > 15.37%, before PSM; RDW > 15.35%, after PSM) was shorter than that of the low-RDW group. Compared with the low-RDW group, the hazard ratios of the high-RDW group were 3.0708 (before PSM) and 1.4197 (after PSM). RDW is an independent risk factor for in-hospital mortality in patients with AP. The predictive value of RDW for in-hospital mortality of patients with AP is comparable to BISAP, and the combination of RDW and BISAP or SOFA scoring system can improve the predictive performance to a certain extent.

Prognostic value of inflammatory markers for in-hospital mortality in intensive care patients with acute ischemic stroke: a retrospective observational study based on MIMIC-IV.

Acute ischemic stroke (AIS) is a primary cause of death and disability worldwide. Four markers that can be readily determined from peripheral blood, namely, the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and total bilirubin, were measured in this study. We examined the relationship between the SII and in-hospital mortality after AIS and evaluated which of the above four indicators was most accurate for predicting in-hospital mortality after AIS. We selected patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database who were aged >18 years and who were diagnosed with AIS on admission. We collected the patients' baseline characteristics, including various clinical and laboratory data. To investigate the relationship between the SII and in-hospital mortality in patients with AIS, we employed the generalized additive model (GAM). Differences in in-hospital mortality between the groups were summarized by the Kaplan-Meier survival analysis and the log-rank test. The receiver operating characteristic (ROC) curve analysis was used to assess the accuracy of the four indicators (SII, NLR, PLR, and total bilirubin) for predicting in-hospital mortality in patients with AIS. The study included 463 patients, and the in-hospital mortality rate was 12.31%. The GAM analysis showed a positive correlation between the SII and in-hospital mortality in patients with AIS, but the correlation was not linear. Unadjusted Cox regression identified a link between a high SII and an increased probability of in-hospital mortality. We also found that patients with an SII of >1,232 (Q2 group) had a considerably higher chance of in-hospital mortality than those with a low SII (Q1 group). The Kaplan-Meier analysis demonstrated that patients with an elevated SII had a significantly lower chance of surviving their hospital stay than those with a low SII. According to the results of the ROC curve analysis, the in-hospital mortality of patients with AIS predicted by the SII had an area under the ROC curve of 0.65, which revealed that the SII had a better discriminative ability than the NLR, PLR, and total bilirubin. The in-hospital mortality of patients with AIS and the SII were positively correlated, but not linearly. A high SII was associated with a worse prognosis in patients with AIS. The SII had a modest level of discrimination for forecasting in-hospital mortality. The SII was slightly better than the NLR and significantly better than the PLR and total bilirubin for predicting in-hospital mortality in patients with AIS.

Urine output for predicting in-hospital mortality of intensive care patients with cardiogenic shock

BackgroundThe role of urine output (UO) in the first 24 h of admission in the clinical management of cardiogenic shock (CS) patients has not been elucidated. MethodsThis study retrospectively analyzed intensive care CS patients in the MIMIC-IV database. Binomial logistic regression analysis was conducted to evaluate whether UO was an independent risk factor for in-hospital mortality in CS patients. The performance of UO in predicting mortality was evaluated by the receiver operating characteristic (ROC) curve and compared with the Oxford Acute Severity of Illness Score (OASIS). The clinical net benefit of UO in predicting mortality was determined using the decision curve analysis (DCA). Survival analysis was performed with Kaplan-Meier curves. ResultsAfter adjusting for confounding factors including diuretic use and acute kidney injury (AKI), UO remained an independent risk factor for in-hospital mortality in CS patients. The areas under the ROC curves (AUCs) of UO for predicting in-hospital mortality were 0.712 (UO, ml/day) and 0.701 (UO, ml/kg/h), which were comparable to OASIS (AUC = 0.695). In terms of clinical net benefit, UO was comparable to OASIS, with different degrees of benefit at different threshold probabilities. Survival analysis showed that the risk of in-hospital death in the low-UO (≤857 ml/day) group was 3.0143 times that of the high-UO (>857 ml/day) group. ConclusionsUO in the first 24 h of admission is an independent risk factor for in-hospital mortality in intensive care CS patients and has moderate predictive value in predicting in-hospital mortality.

Association between albumin corrected anion gap and 30-day all-cause mortality of critically ill patients with acute myocardial infarction: a retrospective analysis based on the MIMIC-IV database

BackgroundThe anion gap (AG) has been linked to the prognosis of many cardiovascular disorders. However, the correlation between albumin-corrected anion gap (ACAG) and 30 d all-cause mortality of intensive care patients with acute myocardial infarction (AMI) is unclear. Furthermore, owing to the lack of studies, it is also unknown whether ACAG is more accurate than AG in predicting the mortality of AMI.MethodsThe Medical Information Mart for Intensive Care IV (MIMIC IV) dataset was used to provide patient data in this retrospective cohort study. ACAG is computed using the formulae: [4.4—{albumin (g/dl)}] × 2.5 + AG. The primary outcome was 30 d all-cause mortality intensive care patients with AMI. To explore the prognostic worthiness of ACAG, the receiver operating characteristic curve, smooth curve fitting, Cox regression model, and Kaplan survival analysis was performed.ResultsWe enrolled 2,160 patients in this study. ACAG had a better predictive value for 30 d all-cause mortality than AG, with an area under the curve of 0.66. The association between ACAG levels and overall mortality was nonlinear. In our model, after correcting for confounding factors, the ACAG was the independent predictor for 30 d all-cause mortality (HR 1.75, 95%CI 1.24, 2.47). ACAG K-M estimator curve analyses revealed that the group with ACAG ≥ 21.75 mmol/l had poor survival rate than the other group.ConclusionsHigh serum ACAG levels were a significant risk factor for 30 d all-cause mortality in critically ill patients with AMI. ACAG concentration and 30 d all-cause mortality had a nonlinear relationship. ACAG had better predictive value in identifying 30 d all-cause mortality of patients with AMI in ICU than the AG.

The Association between the Hypochloremia and Mortality in Intensive Care Unit (ICU) Patients with Chronic Heart Failure

Objective: To explore the association between hypochloremia and mortality in critically ill patients with chronic heart failure (CHF). Methods: This is a retrospective cohort study from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database of patients with CHF diagnosed according to ICD-9 or ICD-10. Patients were divided into three groups according to serum chloride values. A multivariable logistic regression analysis was used to investigate the relationship between hypochloremia and short-term mortality. Results: A total of 2103 patients with CHF were enrolled in our study. The 30-day mortality was 6.7%. After adjusting for confounders, the 30-day mortality risks of the hypochloremia group were significantly higher than that of the group with normal serum chloride (OR 2.23, 95% CI 1.27–3.92, p = 0.005). Hypochloremia was consistently associated with increased mortality in patients that were older or had sepsis. Conclusion: Hypochloremia is associated with increased mortality in intensive care patients critically ill with CHF.

Relationship between butritional scores and 28-day mortality in critical patients who received mechanical ventilator support for non-surgical reasons

Aims: Malnutrition may cause an increase in morbidity and mortality in intensive care patients. In this study, we aimed to investigate the relationship between nutritional scores and 28-day mortality in critically ill patients followed on a mechanical ventilator for non-surgical reasons. Methods: 91 patients admitted to the intensive care unit for non-surgical reasons, followed up on mechanical ventilators, and whose data were available were included. The prognostic nutrition index (PNI), geriatric nutrition risk index (GNRI), nutritional risk index (NRI), and controlling nutritional status (CONUT) score were calculated from the data of the patients. Patients were divided into two groups survival and non-survival. Results: NRI, PNI, and GNRI scores were statistically significantly higher in the Survivor group. Neutrophil lymphocyte ratio, LDH albumin ratio, CONUT, APACHE, and SAPS scores were statistically higher in the nonsurvivor group. In logistic regression analysis for nutritional scores, CONUT was found to be an independent risk factor for mortality (table 5). In the ROC analysis, the AUC value for CONUT was 0.925. The cut-off value for CONUT was 7.5, the sensitivity was 86.4%, and the specificity was 87.0%. Conclusion: The CONUT nutrition score, which can be easily calculated from routine parameters and does not cause extra costs, can be used as an independent evaluation tool in determining the 28-day mortality of intensive care patients.

Predictive factors for mortality in intensive care patients with Fournier's gangrene: five years' experience from a single center in Turkey.

The aim of this study was to analyze mortality-related factors and scoring systems in order to better manage the treatment process of patients monitored in the intensive care unit (ICU) due to Fournier's gangrene (FG). The study included 28 male patients who were monitored in the surgical ICU with the diagnosis of FG between December 2018 and August 2022. The patients' comorbidities, acute physiological and chronic health evaluation scoring system II (APACHE II), Fournier gangrene severity index (FGSI), sequential organ failure assessment (SOFA) scores, and laboratory data were evaluated retrospectively. Of the patients, 67.9% (n=19) had diabetes mellitus, 78.6% (n=22) had hypertension, and 71.4% (n=20) had coronary artery disease. The mortality rate was 42% (n=11). There was no statistically significant difference between the patients who died and those who survived in terms of the SOFA score, comorbidities, and albumin, glucose, and procalcitonin values (p > 0.05), but age, APACHE II and FGSI scores, and the C-reactive protein (CRP) value were significantly higher in the non-survivor group. There was a positive correlation between the FGSI, APACHE II, and SOFA scores. Older age, high CRP levels at the time of admission, and the presence of comorbidity are still determining factors in the prediction of mortality in patients with FG. We also determined that in predicting mortality in patients monitored in the ICU with the diagnosis of FG, in addition to the routinely used FGSI, the APACHE II score was also useful, but the SOFA score did not have significant predictive value.

Association between different concentrations of human serum albumin and 28-day mortality in intensive care patients with sepsis: A propensity score matching analysis.

Background: Human serum albumin (HSA) is a commonly used medication for the treatment of sepsis. However, there is no conclusive evidence as to whether different concentrations of HSA are associated with patient prognosis. This study aimed to evaluate the association between different concentrations of HSA and 28-day mortality in patients with sepsis. Methods: The data for this retrospective study were collected from the Medical Information Mart for Intensive Care IV database. Patients with sepsis were divided into two groups according to the concentration of HSA received: 25% and 5% HSA. The primary outcome of this study was the 28-day mortality in patients with sepsis. To ensure the robustness of our findings, we used multivariate Cox regression, propensity score matching, double-robust estimation, and inverse probability weighting models. Results: A total of 76,943 patients were screened, of whom 5,009 were enrolled. 1,258 and 3,751 patients received 25% and 5% HSA, respectively. The 28-day mortality rate was 38.2% (481/1,258) for patients in the 25% HSA group and 8.7% (325/3,751) for patients in the 5% HSA group. After propensity score matching, 1,648 patients were identified. The inverse probability weighting model suggested that 5% HSA received was associated with lower 28-day mortality (hazard ratio [HR]: 0.63, 95% confidence interval [CI]: 0.54-0.73, p < 0.001). Subgroup and sensitivity analysis confirmed the robustness of the results. Conclusion: In patients with sepsis, 5% HSA received may be associated with a lower risk of 28-day mortality than 25% HSA. Further randomized controlled trials are required to confirm this association.

Relationship between Systemic Immune-inflammation Index and Mortality in Intensive Care Patients Diagnosed with Crimean-Congo Hemorrhagic Fever.

To evaluate the power of the systemic immune-inflammation index in the prediction of mortality in severe Crimean-Congo hemorrhagic fever patients. Observational study. Department of Anaesthesiology and Reanimation, Sivas Cumhuriyet University, Sivas, Turkey, from January to June 2022. Intensive care patients diagnosed with Crimean-Congo hemorrhagic fever, between January 2012 and January 2022, were included. Demographic data, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and systemic immune-inflammation index were recorded. Receiver operating characteristic analysis, Cox regression analysis and Kaplan-Meier mortality analyses were done. A cut-off value <1.85 for neutrophil to lymphocyte ratio showed 41.67% sensitivity and 97.06% specificity. A cut-off value <80.75 for the systemic immune-inflammation index showed 84.72% sensitivity and 76.47% specificity. A cut-off value <37.86 for platelet-to- lymphocyte ratio showed 84.72% sensitivity and 73.53% specificity. In patients with systemic immune-inflammation index value <80.75, the mortality rate increased 2.549 times and 3.732 times in patients with a platelet-to-lymphocyte ratio value <37.86. Similar sensitivity and specificity levels were found for systemic immune-inflammation index and platel-to-lymphocyte ratio regarding the mortality prediction power and impact on mortality. Both tests can be used for the prediction of mortality during the hemorrhagic period in patients with severe Crimean-Congo Hemorrhagic Fever. Crimean-congo hemorrhagic fever, Systemic immune inflammation index, Mortality, Platelet-to-lymphocyte ratio.

Diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients: A systematic review

BackgroundNursing data consist of observations of patients' conditions and information on nurses' clinical judgment based on critically ill patients' behavior and physiological signs. Nursing data in electronic health records were recently emphasized as important predictors of patients' deterioration but have not been systematically reviewed. ObjectiveWe conducted a systematic review of prediction models using nursing data for clinical outcomes, such as prolonged hospital stay, readmission, and mortality in intensive care patients, compared to physiological data only. In addition, the type of nursing data used in prediction model developments was investigated. DesignA systematic review. MethodsPubMed, CINAHL, Cochrane CENTRAL, EMBASE, IEEE Xplore Digital Library, Web of Science, and Scopus were searched. Clinical outcome prediction models using nursing data for intensive care patients were included. Clinical outcomes were prolonged hospital stay, readmission, and mortality. Data were extracted from selected studies such as study design, data source, outcome definition, sample size, predictors, reference test, model development, model performance, and evaluation. The risk of bias and applicability was assessed using the Prediction model Risk of Bias Assessment Tool checklist. Descriptive summaries were produced based on paired forest plots and summary receiver operating characteristic curves. ResultsSixteen studies were included in the systematic review. The data types of predictors used in prediction models were categorized as physiological data, nursing data, and clinical notes. The types of nursing data consisted of nursing notes, assessments, documentation frequency, and flowsheet comments. The studies using physiological data as a reference test showed higher predictive performance in combined data or nursing data than in physiological data. The overall risk of bias indicated that most of the included studies have a high risk. ConclusionsThis study was conducted to identify and review the diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients. Most of the included studies developed models using nursing notes, and other studies used nursing assessments, documentation frequency, and flowsheet comments. Although the findings need careful interpretation due to the high risk of bias, the area under the curve scores of nursing data and combined data were higher than physiological data alone. It is necessary to establish a strategy in prediction modeling to utilize nursing data, clinical notes, and physiological data as predictors, considering the clinical context rather than physiological data alone. RegistrationThe protocol for this study is registered with PROSPERO (registration number: CRD42021273319).