Sepsis-related Organ Failure Assessment Research Articles

Comparative analysis of Glasgow Coma Scale, quick Sepsis-related Organ Failure Assessment, base excess, and lactate for mortality prediction in critically ill emergency department patients

Abstract OBJECTIVES: It is crucial to promptly identify high-mortality patients in emergency departments and initiate their treatment as soon as possible. Although many parameters have been studied to select patients with high mortality, no comprehensive evaluation exists in previous literature on these parameters in critically ill patients, regardless of patient groups. The aim of this study is to evaluate the Glasgow Coma Scale (GCS), quick Sepsis-related Organ Failure Assessment (qSOFA), blood gas base excess (BE), and blood gas lactate in predicting mortality in critically ill patients admitted to the emergency department. METHODS: This prospective observational cohort study included adult patients with Emergency Severity Index 1–2 (critically ill) admitted to the emergency department. All patients were evaluated by the physician within 10 min, and blood gas samples were taken. The data collection forms recorded the patients’ GCS and qSOFA scores at the time of first evaluation by the physician. The qSOFA score assessment was performed in all patients with ESI levels 1 and 2, regardless of whether infective pathology was suspected. Blood gas BE and lactate values were also from laboratory test results. Patients or their relatives were contacted by phone at the end of the 1st month to obtain information about the clinical condition (survival or mortality). RESULTS: A total of 868 patients were included, with 163 deaths observed within 30 days. GCS score, qSOFA score, and lactate value were significant in predicting mortality within 30 days. While the BE value was significant for predicting 30-day mortality for values equal to or below the lower limit of −1.5 (P < 0.001), it was not significant for values equal to or above the upper limit of +3 (P > 0.05). The most successful prediction model for predicting 30-day mortality was found to be qSOFA with a cutoff value of ≥1. CONCLUSION: In emergency departments, each of the GCS, qSOFA scores, BE, and lactate values can be used independently as a practical mortality prediction model in critically ill patients. Among these four models, qSOFA is the most successful practical mortality prediction model in critically ill patients.

C-reactive protein compared to procalcitonin in guiding of anti-microbial stoppage in patients with septic shock

BackgroundOne of the greatest and most effective strategies to decrease the likelihood of discovering antibiotic-resistant bacteria in patients receiving critical care is to shorten the duration of antibiotic therapy.ObjectivesTo assess the utility of procalcitonin compared to traditional inflammation markers like C-reactive protein in an antimicrobial stoppage in patients with septic shock.MethodsThis was a comparative, prospective, randomized, observer-blind clinical experiment conducted on 60 septic patients hospitalized in intensive care units at Benha University hospitals between May 2021 and May 2022. Groups for PCT and CRP patients were separated. The full clinical history, co-morbidities that were related, and patient history were recorded. The baseline PCT and CRP values were determined on days 4, 7, 10, and 14. They were contrasted with sepsis ratings obtained from the Acute Physiology and Chronic Health Evaluation II (APACHE II) and the Sepsis-related Organ Failure Assessment (SOFA). Days 4, 7, and 10 were used to evaluate the antibiotic's efficacy.ResultsThere was no correlation between CRP levels and APACHE II and SOFA scores on days 1, 4, and 7, but on days 7 and 10, PCT levels were strongly linked with both (P < 0.05). PCT was linked to lower antibacterial exposure (23.3% of PCT participants ceased taking antibiotics on day 4, compared to 6.6% of CRP participants; P = 0.07). After 10 days, 30% of the PCT group and 70% of the CRP group kept up their antimicrobial regimen (P = 0.0001).ConclusionProcalcitonin dramatically reduced the duration of antimicrobial treatment. Procalcitonin use has reduced hospital expenses, complications of extended hospital stay, side effects of excessive antibiotic use, and hence, the mortality rate in septic patients.

Impact of Abnormal Ankle Brachial Index on Sepsis Survival: One-Year Prospective Study Results.

Lower extremity peripheral artery disease (LE-PAD) has been linked to unfavorable cardiovascular outcomes. The impact of potentially undiagnosed LE-PAD, suspected by abnormal ankle-brachial index (ABI), on the survival of sepsis patients admitted to the intensive care unit (ICU) remains uncertain. We conducted a prospective cohort study and recruited adult patients admitted to the ICU with a primary diagnosis of sepsis (defined by a quick Sepsis-Related Organ Failure Assessment score of ≥ 2) between November 23, 2017 and July 22, 2018. ABI measurements were obtained within 24 hours of admission. The study compared the 30-day and 1-year all-cause mortality rates as well as the incidence of major adverse cardiovascular events (MACEs) between the groups with normal and abnormal ABI values. Of the 102 sepsis patients admitted to the ICU, 38 (37%) were diagnosed with LE-PAD based on their ABI measurements. The overall 30-day mortality rate was 30.0% in patients with LE-PAD and 25.8% in those with normal ABI (p = 0.56). At 1 year, the overall mortality rate was 52.6% in the patients with abnormal ABI and 40.6% in those with normal ABI (p = 0.24). Additionally, the incidence of MACEs was significantly higher in the patients with abnormal ABI compared to those with normal ABI at 1-year follow-up (21.1% vs. 3.1%, respectively; p = 0.003). The patients with abnormal ABI had a higher incidence of MACEs within one year following hospital discharge. Future studies are needed to improve cardiovascular outcomes among sepsis survivors (ClinicalTrials.gov number, NCT03372330).

Usefulness of the qSOFA score for predicting hospital mortality in cancer patients

Usefulness of the qSOFA score for predicting hospital mortality in cancer patients

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission

BackgroundThis study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission.MethodsData relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension.ResultsA total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables (“sofa (sepsis-related organ failure assessment)”, “minimum glucose”, “maximum sodium”, “age”, “mean spo2 (blood oxygen saturation)”, “maximum temperature”, “maximum heart rate”, “minimum bun (blood urea nitrogen)”, “minimum wbc (white blood cells)” and “charlson comorbidity index”) and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low “maximum temperature” patients.ConclusionsThe 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.

Clinical outcomes and safety of remdesivir in hospitalized individuals with COVID-19, with or without severe renal impairment

BackgroundThe use of remdesivir in patients with coronavirus disease 2019 (COVID-19) and severe renal impairment has been approved; however, limited clinical data exist. Accordingly, we aimed to compare outcomes and adverse events associated with remdesivir in hospitalized patients with COVID-19, with and without severe renal impairment. MethodsHospitalized patients with COVID-19 undergoing a 5-day remdesivir course at Taipei Veterans General Hospital from April 1 to July 31, 2022, were enrolled. Comparative analysis of outcomes and safety between patients with or without severe renal impairment (estimated glomerular filtration rate of < 30 mL/min per 1.73 m2) were conducted. Prognostic factors associated with 28-day mortality in patients with severe renal impairment were investigated using logistic regression analysis. ResultsA total of 671 hospitalized patients, including 132 patients with severe renal impairment, who received a 5-day course of remdesivir were analyzed. The 28-day mortality was higher in patients with severe renal impairment than in patients without severe renal impairment (15.2% vs. 7.8%). The proportion of patients with acute kidney injury (AKI) and deteriorated liver function after completing remdesivir therapy was similar between the patients with and without severe renal impairment, and the recovery rate of AKI was similar in both groups. The sequential organ failure assessment score was an independent factor associated with 28-day mortality in patients with severe renal impairment. ConclusionsRemdesivir was well-tolerated in hospitalized patients with COVID-19, regardless of renal function. Our findings support the recent recommendation to administer remdesivir in patients with severe renal impairment.

Elevations in presepsin, PCT, hs-CRP, and IL-6 levels predict mortality among septic patients in the ICU.

This study aimed to investigate whether changes in presepsin, procalcitonin, high-sensitivity C-reactive protein, and interleukin 6 levels predict mortality in septic patients in the intensive care unit. This study enrolled septic patients between November 2020 and December 2021. Levels of presepsin, procalcitonin, high-sensitivity C-reactive protein, and interleukin 6 were measured on the first (PSEP_0, PCT_0, hsCRP_0, IL-6_0) and third days (PSEP_3, PCT_3, hsCRP_3, IL-6_3). Follow-up was performed on days 3, 7, 14, 21, and 28 after enrollment. The outcome was all-cause death. The study included 119 participants, and the mortality was 18.5%. In univariable Cox proportional hazards regression analysis, ΔPSEP (= PSEP_3 - PSEP_0) > 211.49 pg/mL (hazard ratio, 2.70; 95% confidence interval, 1.17-6.22), ΔPCT (= PCT_3 - PCT_0) > -0.13 ng/mL (hazard ratio, 7.31; 95% confidence interval, 2.68-19.80), ΔhsCRP (= hsCRP_3 - hsCRP_0) > -19.29 mg/L (hazard ratio, 6.89; 95% confidence interval, 1.61-29.40), and ΔIL-6 (= IL-6_3 - IL-6_0) > 1.00 pg/mL (hazard ratio, 3.13; 95% confidence interval, 1.35-7.24) indicated an increased risk of mortality. The composite concordance index for alterations in all 4 distinct biomarkers was highest (concordance index, 0.83; 95% confidence interval, 0.76-0.91), suggesting the optimal performance of this panel in mortality prediction. In decision curve analysis, compared with the Acute Physiology and Chronic Health Evaluation II and Sequential (sepsis-related) Organ Failure Assessment scores, the combination of the 4 biomarkers had a larger net benefit. Interestingly, interleukin 6 was predominantly produced by monocytes upon lipopolysaccharide stimulation in peripheral blood mononuclear cells. ΔPSEP, ΔPCT, ΔhsCRP, and ΔIL-6 are reliable biomarkers for predicting mortality in septic patients in the intensive care unit, and their combination has the best performance.

The Combination of Systemic Immune-Inflammation Index and Serum Procalcitonin has High Auxiliary Predictive Value for Short-Term Adverse Prognosis in Septic Shock Patients

BackgroundSeptic shock is the most serious complication of sepsis, with more secure and efficient biomarkers urgently needed. Systemic immune-inflammation index (SII) and serum procalcitonin (PCT) show involvement in predicting septic shock prognosis. ObjectiveHerein, we explored the clinical value of the SII-PCT combination in the short-term prognosis of septic shock patients. MethodsTotally 200 septic shock patients were analyzed retrospectively and allocated into the survival and death groups upon 28-day in-hospital outcomes. Correlations of SII, PCT, acute physiology and chronic health evaluation II (APACHE II)/sepsis-related organ failure assessment (SOFA) scores, C-reactive protein (CRP), and serum creatinine (Scr) were analyzed using Spearman. The influencing factors of SII and serum PCT for short-term poor prognosis were analyzed using logistic multivariate regression model. The auxiliary predictive value of SII, PCT, and their combination for short-term adverse septic shock prognosis was evaluated by the receiver operating characteristic curve. Differences in the area under the curve (AUC) were compared using MedCalc. ResultsThe death group had higher APACHE II/SOFA scores, LYM, CRP, Scr, SII, and PCT levels than the survival group. SII and PCT were positively correlated with APACHE II and SOFA scores, LYM, CRP, and Scr, and were independent risk factors influencing the adverse septic shock prognosis. The AUC of the SII-PCT combination in predicting short-term adverse septic shock prognosis was 0.893 (0.841-0.932), with 76.12% sensitivity and 87.97% specificity, with the combination showing a higher AUC than SII/PCT alone. ConclusionsThe SII-PCT combination helps predict the adverse prognosis of septic shock patients.

Clinical complexity of an Italian cardiovascular intensive care unit: the role of mortality and severity risk scores.

The identification of patients at greater mortality risk of death at admission into an intensive cardiovascular care unit (ICCU) has relevant consequences for clinical decision-making. We described patient characteristics at admission into an ICCU by predicted mortality risk assessed with noncardiac intensive care unit (ICU) and evaluated their performance in predicting patient outcomes. A total of 202 consecutive patients (130 men, 75 ± 12 years) were admitted into our tertiary-care ICCU in a 20-week period. We evaluated, on the first 24 h data, in-hospital mortality risk according to Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score 3 (SAPS 3); Sepsis related Organ Failure Assessment (SOFA) Score and the Mayo Cardiac intensive care unit Admission Risk Score (M-CARS) were also calculated. Predicted mortality was significantly lower than observed (5% during ICCU and 7% at discharge) for APACHE II and SAPS 3 (17% for both scores). Mortality risk was associated with older age, more frequent comorbidities, severe clinical presentation and complications. The APACHE II, SAPS 3, SOFA and M-CARS had good discriminative ability in distinguishing deaths and survivors with poor calibration of risk scores predicting mortality. In a recent contemporary cohort of patients admitted into the ICCU for a variety of acute and critical cardiovascular conditions, scoring systems used in general ICU had good discrimination for patients' clinical severity and mortality. Available scores preserve powerful discrimination but the overestimation of mortality suggests the importance of specific tailored scores to improve risk assessment of patients admitted into ICCUs.

Early Identification of Sepsis-Induced Acute Kidney Injury by Using Monocyte Distribution Width, Red-Blood-Cell Distribution, and Neutrophil-to-Lymphocyte Ratio.

Sepsis-induced acute kidney injury (AKI) is a common complication in patients with severe illness and leads to increased risks of mortality and chronic kidney disease. We investigated the association between monocyte distribution width (MDW), red-blood-cell volume distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), sepsis-related organ-failure assessment (SOFA) score, mean arterial pressure (MAP), and other risk factors and sepsis-induced AKI in patients presenting to the emergency department (ED). This retrospective study, spanning 1 January 2020, to 30 November 2020, was conducted at a university-affiliated teaching hospital. Patients meeting the Sepsis-2 consensus criteria upon presentation to our ED were categorized into sepsis-induced AKI and non-AKI groups. Clinical parameters (i.e., initial SOFA score and MAP) and laboratory markers (i.e., MDW, RDW, and NLR) were measured upon ED admission. A logistic regression model was developed, with sepsis-induced AKI as the dependent variable and laboratory parameters as independent variables. Three multivariable logistic regression models were constructed. In Model 1, MDW, initial SOFA score, and MAP exhibited significant associations with sepsis-induced AKI (area under the curve [AUC]: 0.728, 95% confidence interval [CI]: 0.668-0.789). In Model 2, RDW, initial SOFA score, and MAP were significantly correlated with sepsis-induced AKI (AUC: 0.712, 95% CI: 0.651-0.774). In Model 3, NLR, initial SOFA score, and MAP were significantly correlated with sepsis-induced AKI (AUC: 0.719, 95% CI: 0.658-0.780). Our novel models, integrating MDW, RDW, and NLR with initial SOFA score and MAP, can assist with the identification of sepsis-induced AKI among patients with sepsis presenting to the ED.

Assessment of a novel BLOOMY score for predicting mortality in hospitalised adults with bloodstream infection

PurposeA German multicentre study BLOOMY was the first to use machine learning approach to develop mortality prediction scores for bloodstream infection (BSI) patients, but the scores have not been assessed in other cohorts. Our aim was to assess how the BLOOMY 14-day and 6-month scores estimate mortality in our cohort of 497 cases with BSI.MethodsClinical data, laboratory data, and patient outcome were gathered retrospectively from patient records. The scores were calculated as presented in the BLOOMY study with the exception in the day of the evaluation.ResultsIn our cohort, BLOOMY 14-day score estimated death by day 14 with an area under curve (AUC) of 0.87 (95% Confidence Interval 0.80–0.94). Using ≥ 6 points as a cutoff, sensitivity was 68.8%, specificity 88.1%, positive predictive value (PPV) 39.3%, and negative predictive value (NPV) 96.2%. These results were similar in the original BLOOMY cohort and outweighed both quick Sepsis-Related Organ Failure Assessment (AUC 0.76) and Pitt Bacteraemia Score (AUC 0.79) in our cohort. BLOOMY 6-month score to estimate 6-month mortality had an AUC of 0.79 (0.73–0.85). Using ≥ 6 points as a cutoff, sensitivity was 98.3%, specificity 10.7%, PPV 25.7%, and NPV 95.2%. AUCs of 6-month score to estimate 1-year and 5-year mortality were 0.80 (0.74–0.85) and 0.77 (0.73–0.82), respectively.ConclusionThe BLOOMY 14-day and 6-month scores performed well in the estimations of mortality in our cohort and exceeded some established scores, but their adoption in clinical work remains to be seen.

Restrictive calcium replacement in septic shock: a multicenter before-after intervention study.

To study the impact of a restrictive calcium replacement protocol in comparison with a liberal one in patients with septic shock. Multicenter retrospective before-after study that estimated the impact of implementing a restrictive calcium replacement protocol in patients with septic shock. Patients admitted to an intensive care unit between May 2019 and April 2021 were assigned to liberal calcium replacement, and those admitted between May 2021 and April 2022 were assigned to a restrictive protocol. The primary outcome measure was 28-day mortality. Patients were matched with propensity scores. A total of 644 patients were included; liberal replacement was used in 453 patients and the restrictive replacement in 191. We paired 553 patients according to propensity scores, 386 in the liberal group and 167 in the restrictive group. Mortality did not differ significantly between the groups at 28 days (35.3% vs 32.3%, respectively; hazard ratio, 0.97; 95% CI, 0.72-1.29) or after resolution of septic shock (81.5% vs 83.8%; hazard ratio, 0.89; 95% CI, 0.73-1.09). Nor did scores on the Sepsis-related Organ Failure Assessment scale differ (2.1 vs 2.6; P = 0.20). The implementation of a restrictive calcium replacement protocol in patients with septic shock was not associated with a decrease in 28-day mortality in comparison with use of a liberal protocol. However, we were able to reduce calcium replacement without adverse effects.

Association between serum creatinine to albumin ratio and short- and long-term all-cause mortality in patients with acute pancreatitis admitted to the intensive care unit: a retrospective analysis based on the MIMIC-IV database.

Serum creatinine (Cr) and albumin (Alb) are important predictors of mortality in individuals with various diseases, including acute pancreatitis (AP). However, most previous studies have only examined the relationship between single Cr or Alb levels and the prognosis of patients with AP. To our knowledge, the association between short- and long-term all-cause mortality in patients with AP and the blood creatinine to albumin ratio (CAR) has not been investigated. Therefore, this study aimed to evaluate the short- and long-term relationships between CAR and all-cause mortality in patients with AP. We conducted a retrospective study utilizing data from the Medical Information Market for Intensive Care (MIMIC-IV) database. The study involved analyzing various mortality variables and obtaining CAR values at the time of admission. The X-tile software was used to determine the optimal threshold for the CAR. Kaplan-Meier (K-M) survival curves and multivariate Cox proportional hazards regression models were used to assess the relationship between CAR and both short- and long-term all-cause mortality. The predictive power, sensitivity, specificity, and area under the curve (AUC) of CAR for short- and long-term mortality in patients with AP after hospital admission were investigated using Receiver Operating Characteristic analysis. Additionally, subgroup analyses were conducted. A total of 520 participants were included in this study. The CAR ideal threshold, determined by X-tile software, was 0.446. The Cox proportional hazards model revealed an independent association between CAR≥0.446 and all-cause mortality at 7-day (d), 14-d, 21-d, 28-d, 90-d, and 1-year (y) before and after adjustment for confounders. K-M survival curves showed that patients with CAR≥0.446 had lower survival rates at 7-d, 14-d, 21-d, 28-d, 90-d, and 1-y. Additionally, CAR demonstrated superior performance, with higher AUC values than Cr, Alb, serum total calcium, Glasgow Coma Scale, Systemic Inflammatory Response Syndrome score, and Sepsis-related Organ Failure Assessment score at 7-d, 14-d, 21-d, 28-d, 90-d, and 1-y intervals. Subgroup analyses showed that CAR did not interact with a majority of subgroups. The CAR can serve as an independent predictor for short- and long-term all-cause mortality in patients with AP. This study enhances our understanding of the association between serum-based biomarkers and the prognosis of patients with AP.

A comparison of Simplified Acute Physiology Score II and Sepsis-related Organ Failure Assessment Score for prediction of mortality after Intensive Care Unit cardiac arrest.

This study investigates the predictive value and suitable cutoff values of the Sepsis-related Organ Failure Assessment Score (SOFA) and Simplified Acute Physiology Score II (SAPS-II) to predict mortality during or after Intensive Care Unit Cardiac Arrest (ICU-CA). In this secondary analysis the ICU database of a German university hospital with five ICU was screened for all ICU-CA between 2016-2019. SOFA and SAPS-II were used for prediction of mortality during ICU-CA, hospital-stay and one-year-mortality. Receiver operating characteristic curves (ROC), area under the ROC (AUROC) and its confidence intervals were calculated. If the AUROC was significant and considered "acceptable," cutoff values were determined for SOFA and SAPS-II by Youden Index. Odds ratios and sensitivity, specificity, positive and negative predictive values were calculated for the cutoff values. A total of 114 (78 male; mean age: 72.8±12.5 years) ICU-CA were observed out of 14,264 ICU-admissions (incidence: 0.8%; 95% CI: 0.7-1.0%). 29.8% (N.=34; 95% CI: 21.6-39.1%) died during ICU-CA. SOFA and SAPS-II were not predictive for mortality during ICU-CA (P>0.05). Hospital-mortality was 78.1% (N.=89; 95% CI: 69.3-85.3%). SAPS-II (recorded within 24 hours before and after ICU-CA) indicated a better discrimination between survival and death during hospital stay than SOFA (AUROC: 0.81 [95% CI: 0.70-0.92] vs. 0.70 [95% CI: 0.58-0.83]). A SAPS-II-cutoff-value of 43.5 seems to be suitable for prognosis of hospital mortality after ICU-CA (specificity: 87.5%, sensitivity: 65.6%; SAPS-II>43.5: 87.5% died in hospital; SAPS-II<43.5: 65.6% survived; odds ratio:13.4 [95% CI: 3.25-54.9]). Also for 1-year-mortality (89.5%; 95% CI: 82.3-94.4) SAPS-II showed a better discrimination between survival and death than SOFA: AUROC: 0.78 (95% CI: 0.65-0.91) vs. 0.69 (95% CI: 0.52-0.87) with a cutoff value of the SAPS-II of 40.5 (specificity: 91.7%, sensitivity: 64.3%; SAPS-II>40.5: 96.4% died; SAPS-II<40.5: 42.3% survived; odd ratio: 19.8 [95% CI: 2.3-168.7]). Compared to SOFA, SAPS-II seems to be more suitable for prediction of hospital and 1-year-mortality after ICU-CA.

Comparison of mortality and outcomes of four respiratory viruses in the intensive care unit: a multicenter retrospective study

This retrospective study aimed to compare the mortality and burden of respiratory syncytial virus (RSV group), SARS-CoV-2 (COVID-19 group), non-H1N1 (Seasonal influenza group) and H1N1 influenza (H1N1 group) in adult patients admitted to intensive care unit (ICU) with respiratory failure. A total of 807 patients were included. Mortality was compared between the four following groups: RSV, COVID-19, seasonal influenza, and H1N1 groups. Patients in the RSV group had significantly more comorbidities than the other patients. At admission, patients in the COVID-19 group were significantly less severe than the others according to the simplified acute physiology score-2 (SAPS-II) and sepsis-related organ failure assessment (SOFA) scores. Using competing risk regression, COVID-19 (sHR = 1.61; 95% CI 1.10; 2.36) and H1N1 (sHR = 1.87; 95% CI 1.20; 2.93) were associated with a statistically significant higher mortality while seasonal influenza was not (sHR = 0.93; 95% CI 0.65; 1.31), when compared to RSV. Despite occurring in more severe patients, RSV and seasonal influenza group appear to be associated with a more favorable outcome than COVID-19 and H1N1 groups.

Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit.

This study aims to develop and validate a nomogram for predicting the risk of bloodstream infections (BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU). Patients' data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training set), the Beijing Friendship Hospital (BFH) database (validation set) and the eICU Collaborative Research Database (eICU-CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram. Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility. The model dataset consisted of 14930 patients (1444 BSI patients) from the MIMIC-IV database, divided into the training and internal validation datasets in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 419 patients (21 with BSI) as the BFH validation dataset. The nomogram was constructed based on Glasgow Coma Scale (GCS), sepsis related organ failure assessment (SOFA) score, temperature, heart rate, respiratory rate, white blood cell (WBC), red width of distribution (RDW), renal replacement therapy and presence of liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness. The nomogram developed in this study provides a valuable tool for clinicians and nurses to assess individual risk, enabling them to identify patients at a high risk of bloodstream infections in the ICU.

Effect of neutrophil to lymphocyte ratio on prognosis of elderly patients with severe sepsis combined with diabetes mellitus

BackgroundTo investigate the predictive value of neutrophil-to-lymphocyte ratio (NLR) in the short-term prognosis of elderly patients with severe sepsis combined with diabetes mellitus (DM).MethodsThe clinical data of 162 elderly patients with severe sepsis combined with DM from January 2018 to December 2022 were retrospectively collected. These patients were divided into a survival group (n = 104) and a death group (n = 58) according to 90-day prognosis. The number of neutrophils, lymphocytes, and NLR were compared. The optimal cut-off value for NLR to predict 90-day prognosis in elderly patients with severe sepsis combined with DM was determined using Receiver Operator Characteristic (ROC) curves, and the patients were divided into high and low NLR groups depending on the optimal cut-off value. The Kaplan-Meier method was used to plot the survival curves of the high and low NLR groups. Risk factors for the 90-day death in elderly patients with severe sepsis combined with DM were analyzed by a multivariate cox regression model.ResultsThere were no significant differences in gender, age, history of hypertension and hyperlipidemia, intensive care unit (ICU) stay, duration of mechanical ventilation, and oxygenation index between the survival group and death group (p > 0.05). However, acute physiological and chronic health evaluation II (APACHE II) scores, and sepsis-related organ failure assessment (SOFA) scores were significantly lower in the survival group compared with the death group (p < 0.05). In the survival group, neutrophils counts and NLR were much lower than those in the death group, while lymphocytes counts were much higher (p < 0.05). ROC curves showed that the optimal cut-off value for NLR to predict 90-day mortality in elderly patients with severe sepsis combined with DM was 3.482. Patients were divided into high NLR and low NLR groups based on whether NLR was ≥ 3.482. In terms of the log-rank test results, patients in the low NLR group had a significantly higher 90-day survival rate than those in the high NLR group (Logrank χ2 = 8.635, p = 0.003). The multivariate cox regression model showed that the length of ICU stay longer than 15 days and NLR ≥ 3.482 were independent risk factors for 90-day prognosis in elderly patients with severe sepsis combined with DM.ConclusionNLR ≥ 3.482 can be used to predict whether poor prognosis occurs in the short term after illness in elderly patients with severe sepsis combined with DM, and has good assessment value.

Construction and evaluation of short-term and long-term mortality risk prediction model for patients with sepsis based on MIMIC-IV database.

Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk. Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model. A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all P<0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% CI 0.710 to 0.724) and 0.716 (95% CI 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value. The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.

The use of muscle ultrasound to detect critical illness myopathy in patients with sepsis: an observational cohort study

BackgroundCritical illness myopathy (CIM) has negative impact on patient outcomes. We aimed to explore the diagnostic value of bedside ultrasonography for early identification of CIM in septic patients and its correlation with other diagnostic methods. This prospective observational study included 40 ICU patients diagnosed with sepsis on admission or within 48 h later according to the third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). They were evaluated using muscle ultrasound, electrodiagnostic and clinical muscle assessment (Medical Research Council, MRC) at two time points, the first was between days 2 and 5 and the second was between days 10 and 15.ResultsThere was significant deterioration of neuromuscular function between the two evaluation points demonstrated by decline in MRC, abnormal nerve conduction and electromyography (EMG) and increased muscle echogenicity on ultrasonography (P ≤ 0.001). Sepsis-Related Organ Failure Assessment (SOFA) score significantly correlated with different neuromuscular assessment tools. MRC had significant correlation with myopathic EMG (P ≤ 0.001, r = − 0.869) and increased muscle echogenicity (P ≤ 0.001, r = − 0.715). Abnormal ultrasonographic muscle architecture had sensitivity of 100%, specificity of 75% and positive likelihood ratio of 4 in detecting muscle dysfunction compared to myopathic EMG.ConclusionsBedside peripheral muscle ultrasound echogenicity grade could be used as an additional screening test in ICU septic patients for early detection of CIM.

Advancing screening tool for hospice needs and end-of-life decision-making process in the emergency department

BackgroundPredicting mortality in the emergency department (ED) is imperative to guide palliative care and end-of-life decisions. However, the clinical usefulness of utilizing the existing screening tools still leaves something to be desired.MethodsWe advanced the screening tool with the A-qCPR (Age, qSOFA (quick sepsis-related organ failure assessment), cancer, Performance Status Scale, and DNR (Do-Not-Resuscitate) risk score model for predicting one-year mortality in the emergency department of Taipei City Hospital of Taiwan with the potential of hospice need and evaluated its performance compared with the existing screening model. We adopted a large retrospective cohort in conjunction with in-time (the trained and the holdout validation cohort) for the development of the A-qCPR model and out-of-time validation sample for external validation and model robustness to variation with the calendar year.ResultsA total of 10,474 patients were enrolled in the training cohort and 33,182 patients for external validation. Significant risk scores included age (0.05 per year), qSOFA ≥ 2 (4), Cancer (5), Eastern Cooperative Oncology Group (ECOG) Performance Status score ≥ 2 (2), and DNR status (2). One-year mortality rates were 13.6% for low (score ≦ 3 points), 29.9% for medium (3 < Score ≦ 9 points), and 47.1% for high categories (Score > 9 points). The AUROC curve for the in-time validation sample was 0.76 (0.74–0.78). However, the corresponding figure was slightly shrunk to 0.69 (0.69–0.70) based on out-of-time validation. The accuracy with our newly developed A-qCPR model was better than those existing tools including 0.57 (0.56–0.57) by using SQ (surprise question), 0.54 (0.54–0.54) by using qSOFA, and 0.59 (0.59–0.59) by using ECOG performance status score. Applying the A-qCPR model to emergency departments since 2017 has led to a year-on-year increase in the proportion of patients or their families signing DNR documents, which had not been affected by the COVID-19 pandemic.ConclusionsThe A-qCPR model is not only effective in predicting one-year mortality but also in identifying hospice needs. Advancing the screening tool that has been widely used for hospice in various scenarios is particularly helpful for facilitating the end-of-life decision-making process in the ED.