Atrial fibrillation is not an independent determinant of 28-day mortality among critically III sepsis patients
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ABSTRACT:
SUBMITTER: Wang W
PROVIDER: S-EPMC10557240 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:This study was performed to explore the characteristics and outcomes of patients with sepsis accompanied by active cancer who were admitted to the intensive care unit (ICU). METHODS:The baseline characteristics, infection profiles, and outcomes of patients with sepsis were retrospectively analyzed according to the presence of concomitant active cancer. The association between concomitant active cancer and 28-day mortality was explored. RESULTS:Of 23,956 patients with sepsis, 1574 (6.6%) had concomitant active cancer. The most common type was digestive (30.7%). The 28-day mortality ranged from 41.9% to 81.5%. Patients with active cancer had a significantly higher Simplified Acute Physiology Score II and significantly shorter length of ICU stay. Respiratory (32.9%), genitourinary (31.0%), and bloodstream (17.0%) infections were most common. Escherichia coli was the most frequent gram-negative pathogenic bacteria. The 28-day mortality rate was significantly higher in patients with than without active cancer. Concomitant active cancer was associated with increased 28-day mortality in patients with sepsis. Hematological malignancy was associated with a significantly higher risk of death than solid tumors. CONCLUSIONS:Concomitant active cancer was associated with higher 28-day mortality in patients with sepsis requiring ICU admission. Hematological malignancy was associated with a higher risk of death than solid tumors.
| S-EPMC6300972 | biostudies-other
Project description:Atrial fibrillation has been associated with increased mortality in critically ill patients. We sought to determine whether atrial fibrillation in the ICU is an independent risk factor for death. A secondary objective was to determine if patients with new-onset atrial fibrillation have different risk factors or outcomes compared with patients with a previous history of atrial fibrillation.Prospective observational cohort study.Medical and general surgical ICUs in a tertiary academic medical center.One thousand seven hundred seventy critically ill patients requiring at least 2 days in the ICU.None.Demographics, medical history, development of atrial fibrillation, fluid balance, echocardiographic findings, medication administration, and hospital mortality were collected during the first 4 days of ICU admission. Atrial fibrillation occurred in 236 patients (13%) (Any AF). Of these, 123 patients (7%) had no prior atrial fibrillation (New-onset AF) while the remaining 113 (6%) had recurrent atrial fibrillation (Recurrent AF). Any AF was associated with male gender, Caucasian race, increased age, cardiac disease, organ failures, and disease severity. Patients with Any AF had increased mortality compared with those without atrial fibrillation (31% vs 17%; p < 0.001), and Any AF was independently associated with death (odds ratio, 1.62; 95% CI, 1.14-2.29; p = 0.007) in multivariable analysis controlling for severity of illness and other confounders. The association of atrial fibrillation with death was magnified in patients without sepsis (odds ratio, 2.92; 95% CI, 1.52-5.60; p = 0.001). Treatment for atrial fibrillation had no effect on hospital mortality. New-onset AF and Recurrent AF were each associated with increased mortality. New-onset AF, but not Recurrent AF, was associated with increased diastolic dysfunction and vasopressor use and a greater cumulative positive fluid balance.Atrial fibrillation in critical illness, whether new-onset or recurrent, is independently associated with increased hospital mortality, especially in patients without sepsis.
| S-EPMC4725582 | biostudies-literature
Project description:ObjectiveMultiple biomarkers are used to assess sepsis severity and prognosis. Increased levels of the soluble receptor for advanced glycation end products (sRAGE) were previously observed in sepsis but also in end-organ injury without sepsis. We evaluated associations between sRAGE and (i) 28-day mortality, (ii) sepsis severity, and (iii) individual organ failure. Traditional biomarkers procalcitonin (PCT), C-reactive protein (CRP) and lactate served as controls.MethodssRAGE, PCT, CRP, and lactate levels were observed on days 1 (D1) and 3 (D3) in 54 septic patients. We also assessed the correlation between the biomarkers and acute respiratory distress syndrome (ARDS), acute kidney injury (AKI) and acute heart failure.ResultsThere were 38 survivors and 16 non-survivors. On D1, non-survivors had higher sRAGE levels than survivors (p = 0.027). On D3, sRAGE further increased only in non-survivors (p < 0.0001) but remained unchanged in survivors. Unadjusted odds ratio (OR) for 28-day mortality was 8.2 (95% CI: 1.02-60.64) for sRAGE, p = 0.048. Receiver operating characteristic analysis determined strong correlation with outcome on D3 (AUC = 0.906, p < 0.001), superior to other studied biomarkers. sRAGE correlated with sepsis severity (p < 0.00001). sRAGE showed a significant positive correlation with PCT and CRP on D3. In patients without ARDS, sRAGE was significantly higher in non-survivors (p < 0.0001) on D3.ConclusionIncreased sRAGE was associated with 28-day mortality in patients with sepsis, and was superior compared to PCT, CRP and lactate. sRAGE correlated with sepsis severity. sRAGE was increased in patients with individual organ failure. sRAGE could be used as an early biomarker in prognostication of outcome in septic patients.
| S-EPMC4044864 | biostudies-literature
Project description:Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.
| S-EPMC8391773 | biostudies-literature
Project description:Background/objectiveThis study was conducted to investigate the clinical characteristics and outcomes of patients with acute ischemic stroke and atrial fibrillation (AF) in intensive care units (ICUs).MethodsIn the Medical Information Mart for Intensive Care IV database, 1,662 patients with acute ischemic stroke were identified from 2008 to 2019. Of the 1,662 patients, 653 had AF. The clinical characteristics and outcomes of patients with and without AF were compared using propensity score matching (PSM). Furthermore, univariate and multivariate Cox regression analyzes were performed.ResultsOf the 1,662 patients, 39.2% had AF. The prevalence of AF in these patients increased in a stepwise manner with advanced age. Patients with AF were older and had higher Charlson Comorbidity Index, CHA2DS2-VASc Score, HAS-BLED score, and Acute Physiology Score III than those without AF. After PSM, 1,152 patients remained, comprising 576 matched pairs in both groups. In multivariate analysis, AF was not associated with higher ICU mortality [hazard ratio (HR), 0.95; 95% confidence interval (CI), 0.64-1.42] or in-hospital mortality (HR, 1.08; 95% CI, 0.79-1.47). In Kaplan-Meier analysis, no difference in ICU or in-hospital mortality was observed between patients with and without AF.ConclusionsAF could be associated with poor clinical characteristics and outcomes; however, it does not remain an independent short-term predictor of ICU and in-hospital mortality among patients with acute ischemic stroke after PSM with multivariate analysis.
| S-EPMC8801535 | biostudies-literature
Project description:BackgroundSepsis is the second-leading cause of death in neonates. We established a predictive nomogram to identify critically ill neonates early and reduce the time to treatment.MethodsIt is a retrospective case-control study based on the MIMIC-III database. The study population comprised 924 neonates diagnosed with sepsis.ResultsNeonates with sepsis included in the MIMIC-III database were enrolled, including 880 surviving neonates and 44 neonates who died. In the derivation dataset, stepwise regression and the Lasso algorithm were employed to select predictive variables, and the neonatal sequential organ failure assessment score (nSOFA) was calculated simultaneously. Bootstrap resampling was utilized to perform internal validation. The results indicated that the Lasso algorithm displayed superior discrimination, sensitivity, and specificity relative to stepwise regression and nSOFA scores. After 500 bootstrap resampling tests, the area under the receiver operating characteristic curve (AUC) of the Lasso algorithm was 0.912 (95% CI: 0.870-0.977). The nomogram based on the Lasso algorithm outperformed stepwise regression and nSOFA scores in terms of calibration and the clinical net benefit. This nomogram can assist in prognosticating neonatal severe sepsis and aid in guiding clinical practice while concurrently improving patient outcomes.ConclusionsThe established nomogram revealed that jaundice, corticosteroid use, weight, serum calcium, inotropes and base excess are all important predictors of 28-day mortality in neonates with sepsis. This nomogram can facilitate the early identification of neonates with severe sepsis. However, it still requires further modification and external validation to make it widely available.
| S-EPMC10560361 | biostudies-literature
Project description:Background and purposeDeep sedation is associated with acute brain dysfunction and increased mortality. We had previously shown that early-assessed brainstem reflexes may predict outcome in deeply sedated patients. The primary objective was to determine whether patterns of brainstem reflexes might predict mortality in deeply sedated patients. The secondary objective was to generate a score predicting mortality in these patients.MethodsObservational prospective multicenter cohort study of 148 non-brain injured deeply sedated patients, defined by a Richmond Assessment sedation Scale (RASS) <-3. Brainstem reflexes and Glasgow Coma Scale were assessed within 24 hours of sedation and categorized using latent class analysis. The Full Outline Of Unresponsiveness score (FOUR) was also assessed. Primary outcome measure was 28-day mortality. A "Brainstem Responses Assessment Sedation Score" (BRASS) was generated.ResultsTwo distinct sub-phenotypes referred as homogeneous and heterogeneous brainstem reactivity were identified (accounting for respectively 54.6% and 45.4% of patients). Homogeneous brainstem reactivity was characterized by preserved reactivity to nociceptive stimuli and a partial and topographically homogenous depression of brainstem reflexes. Heterogeneous brainstem reactivity was characterized by a loss of reactivity to nociceptive stimuli associated with heterogeneous brainstem reflexes depression. Heterogeneous sub-phenotype was a predictor of increased risk of 28-day mortality after adjustment to Simplified Acute Physiology Score-II (SAPS-II) and RASS (Odds Ratio [95% confidence interval] = 6.44 [2.63-15.8]; p<0.0001) or Sequential Organ Failure Assessment (SOFA) and RASS (OR [95%CI] = 5.02 [2.01-12.5]; p = 0.0005). The BRASS (and marginally the FOUR) predicted 28-day mortality (c-index [95%CI] = 0.69 [0.54-0.84] and 0.65 [0.49-0.80] respectively).ConclusionIn this prospective cohort study, around half of all deeply sedated critically ill patients displayed an early particular neurological sub-phenotype predicting 28-day mortality, which may reflect a dysfunction of the brainstem.
| S-EPMC5404790 | biostudies-literature
Project description:IntroductionSepsis is a life-threatening condition that poses a globally high mortality rate. Identifying risk factors is crucial. Insulin resistance and the TYG index, associated with metabolic disorders, may play a role. This study explores their correlation with mortality in non-diabetic septic patients.MethodsThis retrospective cohort study used data from the MIMIC-IV (version 2.1) database, which includes over 50,000 ICU admissions from 2008 to 2019 at Beth Israel Deaconess Medical Center in Boston. We included adult patients with sepsis who were admitted to the intensive care unit in the study. The primary outcome was to evaluate the ability of TYG to predict death at 28-day of hospital admission in patients with sepsis.ResultsThe study included 2213 patients with sepsis, among whom 549 (24.8%) died within 28 days of hospital admission. We observed a non-linear association between TYG and the risk of mortality. Compared to the reference group (lower TYG subgroup), the 28-day mortality increased in the higher TYG subgroup, with a fully adjusted hazard ratio of 2.68 (95% CI: 2.14 to 3.36). The area under the curve (AUC) for TYG was 67.7%, higher than for triglycerides alone (AUC = 64.1%), blood glucose (AUC = 62.4%), and GCS (AUC = 63.6%), and comparable to SOFA (AUC = 69.3%). The final subgroup analysis showed no significant interaction between TYG and each subgroup except for the COPD subgroup (interaction P-values: 0.076-0.548).ConclusionIn our study, TYG can be used as an independent predictor for all-cause mortality due to sepsis within 28 days of hospitalization.
| S-EPMC11302829 | biostudies-literature
Project description:PURPOSE:Sepsis is a common acute life-threatening condition that emergency physicians routinely face. Diagnostic options within the Emergency Department (ED) are limited due to lack of infrastructure, consequently limiting the use of invasive hemodynamic monitoring or imaging tests. The mortality rate due to sepsis can be assessed via multiple scoring systems, for example, mortality in emergency department sepsis (MEDS) score and sepsis patient evaluation in the emergency department (SPEED) score, both of which quantify the variation of mortality rates according to clinical findings, laboratory data, or therapeutic interventions. This study aims to improve the management processes of sepsis patients by comparing SPEED score and MEDS score for predicting the 28-day mortality in cases of emergency sepsis. METHODS:The study is a cross-sectional, prospective study including 61 sepsis patients in ED in Suez Canal University Hospital, Egypt, from August 2017 to June 2018. Patients were selected by two steps: (1) suspected septic patients presenting with at least one of the following abnormal clinical findings: (a) body temperature higher than 38 °C or lower than 36 °C, (b) heart rate higher than 90 beats/min, (c) hyperventilation evidenced by respiratory rate higher than 20 breaths/min or PaCO2 lower than 32 mmHg, and (d) white blood cell count higher than 12,000/μL or lower than 4000/μL; (2) confirmed septic patients with at least a 2-point increase from the baseline total sequential organ failure assessment (SOFA) score following infection. Other inclusion criteria included adult patients with an age ≥18 years regardless of gender and those who had either systemic inflammatory response syndrome or suspected/confirmed infection. Patients were shortly follow-up for the 28-day mortality. Each patient was subject to SPEED score and MEDS score and then the results were compared to detect which of them was more effective in predicting outcome. The receiver operating characteristic curves were also done for MEDS and SPEED scores. RESULTS:Among the 61 patients, 41 died with the mortality rate of 67.2%. The mortality rate increased with a higher SPEED and MEDS scores. Both SPEED and MEDS scores revealed significant difference between the survivors and nonsurvivors (p = 0.004 and p < 0.001, respectively), indicating that both the two systems are effective in predicting the 28-day mortality of sepsis patients. Thereafter, the receiver operating characteristic curves were plotted, which showed that SPEED was better than the MEDS score when applied to the complete study population with an area under the curve being 0.87 (0.788-0.963) as compared with 0.75 (0.634-0.876) for MEDS. Logistic regression analysis revealed that the best fitting predictor of 28-day mortality for sepsis patients was the SPEED scoring system. For every one unit increase in SPEED score, the odds of 28-day mortality increased by 37%. CONCLUSION:SPEED score is more useful and accurate than MEDS score in predicting the 28-day mortality among sepsis patients. Therefore SPEED rather than MEDS should be more widely used in the ED for sepsis patients.
| S-EPMC6921192 | biostudies-literature
Project description:Sepsis is defined by life-threatening organ dysfunction during infection and is one of the leading causes of critical illness. During sepsis, there is high risk that new-onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. As a result, computer aided automated and reliable detection of new-onset AF during sepsis is crucial, especially for the critically ill patients in the intensive care unit (ICU). In this paper, a novel automated and robust two-step algorithm to detect AF from ICU patients using electrocardiogram (ECG) signals is presented. First, several statistical parameters including root mean square of successive differences, Shannon entropy, and sample entropy were calculated from the heart rate for the screening of possible AF segments. Next, Poincaré plot-based features along with P-wave characteristics were used to reduce false positive detection of AF, caused by the premature atrial and ventricular beats. A subset of the Medical Information Mart for Intensive Care (MIMIC) III database containing 198 subjects was used in this study. During the training and validation phases, both the simple thresholding as well as machine learning classifiers achieved very high segment-wise AF classification performance. Finally, we tested the performance of our proposed algorithm using two independent test data sets and compared the performance with two state-of-the-art methods. The algorithm achieved an overall 100% sensitivity, 98% specificity, 98.99% accuracy, 98% positive predictive value, and 100% negative predictive value on the subject-wise AF detection, thus showing the efficacy of our proposed algorithm in critically ill sepsis patients. The annotations of the data have been made publicly available for other investigators.
| S-EPMC7670858 | biostudies-literature