ABSTRACT
BACKGROUND: Diagnosing sepsis remains a challenge because of the lack of gold-standard diagnostics. Since there are no simple, broadly accepted criteria for infection, there is a risk of misclassifying sepsis patients (sepsis mimics) among patients with organ failure. The main objective of this study was to investigate the proportion of non-infected patients (sepsis mimics) in ICU patients with presumed sepsis at intensive care unit (ICU) admission. METHODS: Adult patients were screened retrospectively during 3.5 years in four ICUs in Sweden for fulfilment of the sepsis-3 criteria at ICU admission (presumed sepsis). Proxy criteria for suspected infection were sampled blood culture(s) and concomitant antibiotic administration. Culture-negative presumed sepsis patients were screened for infection according to the Linder-Mellhammar Criteria of Infection (LMCI). Sepsis mimics were defined as without probable infection according to the LMCI. Confirmed sepsis was defined as presumed sepsis after the exclusion of sepsis mimics. RESULTS: In the ICU presumed sepsis cohort (2664 patients), 25% were considered sepsis mimics. The most common reasons for ICU admission among sepsis mimics were acute heart failure and unspecific respiratory failure. Comparing sepsis mimics and confirmed sepsis showed that confirmed sepsis patients were slightly more severely ill but had similar mortality. C-reactive protein had modest discriminatory power (AUROC 0.71) with confirmed sepsis as the outcome. CONCLUSIONS: One-fourth of a presumed ICU sepsis population identified with the sepsis-3 criteria could be considered sepsis mimics. The high proportion of sepsis mimics has a potential dilutional effect on the presumed sepsis population, which threatens the validity of results from sepsis studies using recommended sepsis criteria.
Subject(s)
Intensive Care Units , Sepsis , Humans , Sepsis/diagnosis , Sepsis/mortality , Retrospective Studies , Male , Female , Middle Aged , Aged , Intensive Care Units/statistics & numerical data , Sweden/epidemiology , Aged, 80 and over , Adult , Diagnosis, DifferentialABSTRACT
BACKGROUND: The purpose was to evaluate glial fibrillary acidic protein (GFAP) and total-tau in plasma as predictors of poor neurological outcome after out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA), including comparisons with neurofilament light (NFL) and neuron-specific enolase (NSE). METHODS: Retrospective multicentre observational study of patients admitted to an intensive care unit (ICU) in three hospitals in Sweden 2014-2018. Blood samples were collected at ICU admission, 12 h, and 48 h post-cardiac arrest. Poor neurological outcome was defined as Cerebral Performance Category 3-5 at 2-6 months after cardiac arrest. Plasma samples were retrospectively analysed for GFAP, tau, and NFL. Serum NSE was analysed in clinical care. Prognostic performances were tested with the area under the receiver operating characteristics curve (AUC). RESULTS: Of the 428 included patients, 328 were OHCA, and 100 were IHCA. At ICU admission, 12 h and 48 h post-cardiac arrest, GFAP predicted neurological outcome after OHCA with AUC (95% CI) 0.76 (0.70-0.82), 0.86 (0.81-0.90) and 0.91 (0.87-0.96), and after IHCA with AUC (95% CI) 0.77 (0.66-0.87), 0.83 (0.74-0.92) and 0.83 (0.71-0.95). At the same time points, tau predicted outcome after OHCA with AUC (95% CI) 0.72 (0.66-0.79), 0.75 (0.69-0.81), and 0.93 (0.89-0.96) and after IHCA with AUC (95% CI) 0.61 (0.49-0.74), 0.68 (0.56-0.79), and 0.77 (0.65-0.90). Adding the change in biomarker levels between time points did not improve predictive accuracy compared to the last time point. In a subset of patients, GFAP at 12 h and 48 h, as well as tau at 48 h, offered similar predictive value as NSE at 48 h (the earliest time point NSE is recommended in guidelines) after both OHCA and IHCA. The predictive performance of NFL was similar or superior to GFAP and tau at all time points after OHCA and IHCA. CONCLUSION: GFAP and tau are promising biomarkers for neuroprognostication, with the highest predictive performance at 48 h after OHCA, but not superior to NFL. The predictive ability of GFAP may be sufficiently high for clinical use at 12 h after cardiac arrest.
Subject(s)
Out-of-Hospital Cardiac Arrest , Humans , Glial Fibrillary Acidic Protein , Retrospective Studies , Intermediate Filaments , Prognosis , BiomarkersABSTRACT
Background: The aim of this study was to investigate the development of fatigue and mental illness between 3 and 12 months after critical COVID-19 and explore risk factors for long-lasting symptoms. Study Design and Methods: A prospective, multicenter COVID-19 study in southern Sweden, including adult patients (≥18 years) with rtPCR-confirmed COVID-19 requiring intensive care. Survivors were invited to a follow-up at 3 and 12 months, where patient-reported symptoms were assessed using the Modified Fatigue Impact Scale (MFIS), the Hospital Anxiety and Depression Scale (HADS) and the Posttraumatic Stress Disorder Checklist version 5 (PCL-5). The development between 3 and 12 months was described by changes in relation to statistical significance and suggested values for a minimally important difference (MID). Potential risk factors for long-lasting symptoms were analyzed by multivariable logistic regression. Results: At the 3-month follow-up, 262 survivors (87%) participated, 215 (72%) returned at 12 months. Fatigue was reported by 50% versus 40%, with a significant improvement at 12 months (MFIS; median 38 vs. 33, P < .001, MID ≥4). There were no significant differences in symptoms of mental illness between 3 and 12 months, with anxiety present in 33% versus 28%, depression in 30% versus 22%, and posttraumatic stress disorder in 17% versus 13%. A worse functional outcome and less sleep compared to before COVID-19 were risk factors for fatigue and mental illness at 12 months. Conclusions: Fatigue improved between 3 and 12 months but was still common. Symptoms of mental illness remained unchanged with anxiety being the most reported. A worse functional outcome and less sleep compared to before COVID-19 were identified as risk factors for reporting long-lasting symptoms.
ABSTRACT
BACKGROUND: Previous studies have reported high prognostic accuracy of circulating neurofilament light (NfL) at 24-72 h after out-of-hospital cardiac arrest (OHCA), but performance at earlier time points and after in-hospital cardiac arrest (IHCA) is less investigated. We aimed to assess plasma NfL during the first 48 h after OHCA and IHCA to predict long-term outcomes. METHODS: Observational multicentre cohort study in adults admitted to intensive care after cardiac arrest. NfL was retrospectively analysed in plasma collected on admission to intensive care, 12 and 48 h after cardiac arrest. The outcome was assessed at two to six months using the Cerebral Performance Category (CPC) scale, where CPC 1-2 was considered a good outcome and CPC 3-5 a poor outcome. Predictive performance was measured with the area under the receiver operating characteristic curve (AUROC). RESULTS: Of 428 patients, 328 (77%) suffered OHCA and 100 (23%) IHCA. Poor outcome was found in 68% of OHCA and 55% of IHCA patients. The overall prognostic performance of NfL was excellent at 12 and 48 h after OHCA, with AUROCs of 0.93 and 0.97, respectively. The predictive ability was lower after IHCA than OHCA at 12 and 48 h, with AUROCs of 0.81 and 0.86 (p ≤ 0.03). AUROCs on admission were 0.77 and 0.67 after OHCA and IHCA, respectively. At 12 and 48 h after OHCA, high NfL levels predicted poor outcome at 95% specificity with 70 and 89% sensitivity, while low NfL levels predicted good outcome at 95% sensitivity with 71 and 74% specificity and negative predictive values of 86 and 88%. CONCLUSIONS: The prognostic accuracy of NfL for predicting good and poor outcomes is excellent as early as 12 h after OHCA. NfL is less reliable for the prediction of outcome after IHCA.
Subject(s)
Cardiopulmonary Resuscitation , Out-of-Hospital Cardiac Arrest , Humans , Retrospective Studies , Cohort Studies , Intermediate Filaments , PrognosisABSTRACT
Severe coronavirus disease 2019 (COVID-19) can result in pneumonia and acute respiratory failure. Accumulation of mucus in the airways is a hallmark of the disease and can result in hypoxemia. Here, we show that quantitative proteome analysis of the sputum from severe patients with COVID-19 reveal high levels of neutrophil extracellular trap (NET) components, which was confirmed by microscopy. Extracellular DNA from excessive NET formation can increase sputum viscosity and lead to acute respiratory distress syndrome. Recombinant human DNase (Pulmozyme; Roche) has been shown to be beneficial in reducing sputum viscosity and improve lung function. We treated five patients pwith COVID-19 resenting acute symptoms with clinically approved aerosolized Pulmozyme. No adverse reactions to the drug were seen, and improved oxygen saturation and recovery in all severely ill patients with COVID-19 was observed after therapy. Immunofluorescence and proteome analysis of sputum and blood plasma samples after treatment revealed a marked reduction of NETs and a set of statistically significant proteome changes that indicate reduction of hemorrhage, plasma leakage and inflammation in the airways, and reduced systemic inflammatory state in the blood plasma of patients. Taken together, the results indicate that NETs contribute to acute respiratory failure in COVID-19 and that degrading NETs may reduce dependency on external high-flow oxygen therapy in patients. Targeting NETs using recombinant human DNase may have significant therapeutic implications in COVID-19 disease and warrants further studies.
Subject(s)
COVID-19 Drug Treatment , Deoxyribonuclease I/pharmacology , Extracellular Traps/metabolism , Proteome/analysis , Aged , Blood Proteins/analysis , COVID-19/metabolism , COVID-19/therapy , Female , Fluorescent Antibody Technique , Humans , Male , Middle Aged , Recombinant Proteins/pharmacology , Severity of Illness Index , Sputum/drug effects , Sputum/metabolism , Sputum/virology , Systemic Inflammatory Response Syndrome/drug therapy , Systemic Inflammatory Response Syndrome/metabolism , Systemic Inflammatory Response Syndrome/virologyABSTRACT
BACKGROUND: Traditional models to predict intensive care outcomes do not perform well in COVID-19. We undertook a comprehensive study of factors affecting mortality and functional outcome after severe COVID-19. METHODS: In this prospective multicentre cohort study, we enrolled laboratory-confirmed, critically ill COVID-19 patients at six ICUs in the Skåne Region, Sweden, between May 11, 2020, and May 10, 2021. Demographics and clinical data were collected. ICU burden was defined as the total number of ICU-treated COVID-19 patients in the region on admission. Surviving patients had a follow-up at 90 days for assessment of functional outcome using the Glasgow Outcome Scale-Extended (GOSE), an ordinal scale (1-8) with GOSE ≥5 representing a favourable outcome. The primary outcome was 90-day mortality; the secondary outcome was functional outcome at 90 days. RESULTS: Among 498 included patients, 74% were male with a median age of 66 years and a median body mass index (BMI) of 30 kg/m2 . Invasive mechanical ventilation was employed in 72%. Mortality in the ICU, in-hospital and at 90 days was 30%, 38% and 39%, respectively. Mortality increased markedly at age 60 and older. Increasing ICU burden was independently associated with a two-fold increase in mortality. Higher BMI was not associated with increased mortality. Besides age and ICU burden, smoking status, cortisone use, Pa CO2 >7 kPa, and inflammatory markers on admission were independent factors of 90-day mortality. Lower GOSE at 90 days was associated with a longer stay in the ICU. CONCLUSION: In critically ill COVID-19 patients, the 90-day mortality was 39% and increased considerably at age 60 or older. The ICU burden was associated with mortality, whereas a high BMI was not. A longer stay in the ICU was associated with unfavourable functional outcomes at 90 days.
Subject(s)
COVID-19 , Humans , Male , Aged , Middle Aged , Female , COVID-19/therapy , SARS-CoV-2 , Cohort Studies , Prospective Studies , Critical Illness , Intensive Care UnitsABSTRACT
BACKGROUND: The ABO and RhD blood group represent antigens on the surface of erythrocytes. The ABO blood group antigens are also present on multiple other cells. Interestingly, previous studies have demonstrated associations between the blood group and many types of disease. The present study aimed to identifying associations between the ABO blood group, the RhD blood group, and morbidity and mortality in a mixed cohort and in six pre-defined subgroups of critically ill patients. METHODS: Adult patients admitted to any of the five intensive care units (ICUs) in the Scania Region, Sweden, between February 2007 and April 2021 were eligible for inclusion. The outcomes were mortality analysed at 28- and 90-days as well as at the end of observation and morbidity measured using days alive and free of (DAF) invasive ventilation (DAF ventilation) and DAF circulatory support, including vasopressors or inotropes (DAF circulation), maximum Sequential Organ Failure Assessment score (SOFAmax) the first 28 days after admission and length of stay. All outcomes were analysed in separate multivariable regression models adjusted for age and sex. In addition, in a sensitivity analysis, five subgroups of patients with the main diagnoses sepsis, septic shock, acute respiratory distress syndrome, cardiac arrest and trauma were analysed using the same separate multivariable regression models. RESULTS: In total, 29,512 unique patients were included in the analyses. There were no significant differences for any of the outcomes between non-O blood groups and blood group O, or between RhD blood groups. In the sensitivity analysis of subgroups, there were no differences in mortality between non-O blood groups and blood group O or between the RhD blood groups. AB was the most common blood group in the COVID-19 cohort. CONCLUSIONS: The ABO and RhD blood group do not influence mortality or morbidity in a general critically ill patient population.
Subject(s)
COVID-19 , Critical Illness , ABO Blood-Group System , Adult , Humans , Intensive Care Units , MorbidityABSTRACT
BACKGROUND: Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain injury, cardiac injury, and systemic inflammation, all yield some prognostic value. We hypothesised that cumulative information obtained during the first three days of intensive care could produce a reliable model for predicting neurological outcome following out-of-hospital cardiac arrest (OHCA) using artificial neural network (ANN) with and without biomarkers. METHODS: We performed a post hoc analysis of 932 patients from the Target Temperature Management trial. We focused on comatose patients at 24, 48, and 72 h post-cardiac arrest and excluded patients who were awake or deceased at these time points. 80% of the patients were allocated for model development (training set) and 20% for internal validation (test set). To investigate the prognostic potential of different levels of biomarkers (clinically available and research-grade), patients' background information, and intensive care observation and treatment, we created three models for each time point: (1) clinical variables, (2) adding clinically accessible biomarkers, e.g., neuron-specific enolase (NSE) and (3) adding research-grade biomarkers, e.g., neurofilament light (NFL). Patient outcome was the dichotomised Cerebral Performance Category (CPC) at six months; a good outcome was defined as CPC 1-2 whilst a poor outcome was defined as CPC 3-5. The area under the receiver operating characteristic curve (AUROC) was calculated for all test sets. RESULTS: AUROC remained below 90% when using only clinical variables throughout the first three days in the ICU. Adding clinically accessible biomarkers such as NSE, AUROC increased from 82 to 94% (p < 0.01). The prognostic accuracy remained excellent from day 1 to day 3 with an AUROC at approximately 95% when adding research-grade biomarkers. The models which included NSE after 72 h and NFL on any of the three days had a low risk of false-positive predictions while retaining a low number of false-negative predictions. CONCLUSIONS: In this exploratory study, ANNs provided good to excellent prognostic accuracy in predicting neurological outcome in comatose patients post OHCA. The models which included NSE after 72 h and NFL on all days showed promising prognostic performance.
Subject(s)
Neural Networks, Computer , Out-of-Hospital Cardiac Arrest/mortality , Risk Assessment/methods , Adult , Aged , Area Under Curve , Biomarkers/analysis , Female , Humans , Male , Middle Aged , Out-of-Hospital Cardiac Arrest/complications , Out-of-Hospital Cardiac Arrest/epidemiology , Prognosis , Prospective Studies , ROC Curve , Retrospective Studies , Risk Assessment/standards , Risk Assessment/statistics & numerical dataABSTRACT
BACKGROUND: Biomarkers can be of help to understand critical illness and to identify and stratify sepsis. Adrenomedullin is a vasoactive hormone, with reported prognostic and potentially therapeutic value in sepsis. The primary aim of this study was to investigate the association of circulating bioactive adrenomedullin (bio-ADM) levels at intensive care unit (ICU) admission with mortality in sepsis patients and in a general ICU population. Secondary aims included the association of bio-ADM with organ failure and the ability of bio-ADM to identify sepsis. METHODS: In this retrospective observational study, adult patients admitted to one of four ICUs during 2016 had admission bio-ADM levels analysed. Age-adjusted odds ratios (OR) with 95% CI for log-2 transformed bio-ADM, and Youden's index derived cut-offs were calculated. The primary outcome was 30-day mortality, and secondary outcomes included the need for organ support and the ability to identify sepsis. RESULTS: Bio-ADM in 1867 consecutive patients were analysed; 632 patients fulfilled the sepsis-3 criteria of whom 267 had septic shock. The median bio-ADM in the entire ICU population was 40 pg/mL, 74 pg/mL in sepsis patients, 107 pg/mL in septic shock and 29 pg/mL in non-septic patients. The association of elevated bio-ADM and mortality in sepsis patients and the ICU population resulted in ORs of 1.23 (95% CI 1.07-1.41) and 1.22 (95% CI 1.12-1.32), respectively. The association with mortality remained after additional adjustment for lactate in sepsis patients. Elevated bio-ADM was associated with an increased need for dialysis with ORs of 2.28 (95% CI 2.01-2.59) and 1.97 (95% CI 1.64-2.36) for the ICU population and sepsis patients, respectively, and with increased need of vasopressors, OR 1.33 (95% CI 1.23-1.42) (95% CI 1.17-1.50) for both populations. Sepsis was identified with an OR of 1.78 (95% CI 1.64-1.94) for bio-ADM, after additional adjustment for severity of disease. A bio-ADM cut-off of 70 pg/mL differentiated between survivors and non-survivors in sepsis, but a Youden's index derived threshold of 108 pg/mL performed better. CONCLUSIONS: Admission bio-ADM is associated with 30-day mortality and organ failure in sepsis patients as well as in a general ICU population. Bio-ADM may be a morbidity-independent sepsis biomarker.
Subject(s)
Adrenomedullin/analysis , Sepsis/blood , Shock, Septic/blood , Adrenomedullin/blood , Aged , Biomarkers/analysis , Biomarkers/blood , Critical Illness , Electronic Health Records/statistics & numerical data , Female , Humans , Logistic Models , Male , Middle Aged , Odds Ratio , Organ Dysfunction Scores , Prognosis , Retrospective Studies , Sepsis/diagnosis , Shock, Septic/diagnosis , SwedenABSTRACT
BACKGROUND: Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis, tailor therapy and help in interpreting the intervention effect in heterogenous clinical trials. This study aimed to create a model for early prediction of outcome by artificial neural networks (ANN) and use this model to investigate intervention effects on classes of illness severity in cardiac arrest patients treated with targeted temperature management (TTM). METHODS: Using the cohort of the TTM trial, we performed a post hoc analysis of 932 unconscious patients from 36 centres with OHCA of a presumed cardiac cause. The patient outcome was the functional outcome, including survival at 180 days follow-up using a dichotomised Cerebral Performance Category (CPC) scale with good functional outcome defined as CPC 1-2 and poor functional outcome defined as CPC 3-5. Outcome prediction and severity class assignment were performed using a supervised machine learning model based on ANN. RESULTS: The outcome was predicted with an area under the receiver operating characteristic curve (AUC) of 0.891 using 54 clinical variables available on admission to hospital, categorised as background, pre-hospital and admission data. Corresponding models using background, pre-hospital or admission variables separately had inferior prediction performance. When comparing the ANN model with a logistic regression-based model on the same cohort, the ANN model performed significantly better (p = 0.029). A simplified ANN model showed promising performance with an AUC above 0.852 when using three variables only: age, time to ROSC and first monitored rhythm. The ANN-stratified analyses showed similar intervention effect of TTM to 33 °C or 36 °C in predefined classes with different risk of a poor outcome. CONCLUSION: A supervised machine learning model using ANN predicted neurological recovery, including survival excellently, and outperformed a conventional model based on logistic regression. Among the data available at the time of hospitalisation, factors related to the pre-hospital setting carried most information. ANN may be used to stratify a heterogenous trial population in risk classes and help determine intervention effects across subgroups.
Subject(s)
Critical Care , Hypothermia, Induced , Neural Networks, Computer , Out-of-Hospital Cardiac Arrest/therapy , Aged , Female , Hospitalization , Humans , Male , Middle Aged , Prognosis , ROC Curve , Reproducibility of Results , Risk AssessmentABSTRACT
BACKGROUND: It is well known that low platelet count on admission to intensive care units (ICU) is associated with increased mortality. However, it is unknown whether prothrombin time (PT-INR) and activated partial thromboplastin time (APTT) on admission correlate with mortality and organ failure. Therefore, the aim of this study was to investigate whether PT-INR and APTT at admission can predict outcome in the critically ill patient after adjusting for severity of illness measured with Simplified Acute Physiology Score 3 (SAPS 3). MATERIALS AND METHODS: Data were retrospectively collected. APTT and PT-INR taken on admission and SAPS 3 score were independent variables in all regression analyses. Survival analysis was done with Cox regression. Organ failure was reported as days alive and free (DAF) of vasopressors and invasive ventilation, need of continuous renal replacement therapy (CRRT) and Acute Kidney Injury Network creatinine score (AKIN-crea). RESULTS: A total of 3585 ICU patients were included. Prolonged APTT correlated with mortality with 95% confidence interval (CI) of hazard ratio 1.001-1.010. Prolonged APTT also correlated with DAF vasopressor, CRRT and AKIN-crea with 95% CI of odds ratio (OR) 1.009-1.034, 1.016-1.037 and 1.009-1.028, respectively. Increased PT-INR correlated with DAF vasopressor and DAF ventilator with 95% CI of OR 1.112-2.014 and 1.135-1.847, respectively. CONCLUSIONS: Activated partial thromboplastin time prolongation was associated with mortality and all morbidity outcomes except the DAF ventilator. PT-INR increase at admission was associated with DAF vasopressor and DAF ventilator. APTT and PT-INR at admission correlate with morbidity, which is not accounted for in the SAPS 3 model.
Subject(s)
Multiple Organ Failure/mortality , Prothrombin Time/mortality , Prothrombin Time/statistics & numerical data , Aged , Blood Coagulation Tests , Cohort Studies , Critical Illness/mortality , Female , Humans , Intensive Care Units , Male , Middle Aged , Partial Thromboplastin Time/mortality , Partial Thromboplastin Time/statistics & numerical data , Predictive Value of Tests , Retrospective Studies , Risk Factors , Survival Analysis , Sweden/epidemiologyABSTRACT
BACKGROUND: Sepsis is a common indication for admission to the intensive care unit (ICU). Since definitions vary across studies, comparisons of prevalence and outcomes have been challenging. We aimed to compare sepsis according to ICU discharge codes with sepsis according to Sepsis-3 criteria and to investigate the epidemiology of sepsis in the ICU. We hypothesized that sepsis using discharge codes is underreported. METHODS: Adult ICU admissions to four ICUs in Sweden between 2015 and 2017 were screened for sepsis according to the Sepsis-3 criteria. Medical records were reviewed and data extracted from the Swedish Intensive Care Registry. RESULTS: Of 5990 adult ICU patients, 28% fulfilled the Sepsis-3 criteria on admission, but only 31% of them had sepsis as the registered main diagnosis at ICU discharge. Of the 1654 Sepsis-3 patients, 38% met the septic shock criteria. The Sepsis-3 in-hospital mortality was 26% compared to 33% in patients with septic shock. The incidence rate for ICU-treated sepsis was 81 cases per 100 000 person-years. One in four had a positive blood culture, and 44% were culture negative. CONCLUSION: This large Swedish multicentre study showed that 28% of adult ICU patients fulfilled the Sepsis-3 criteria, but only one third of them had sepsis according to ICU discharge codes. We could confirm our hypothesis, that sepsis is severely underreported in Swedish ICUs, and we conclude that discharge codes should not be used for quality control or research purposes.
Subject(s)
Intensive Care Units , Sepsis/epidemiology , Aged , Female , Humans , Incidence , Male , Middle Aged , Retrospective Studies , Sweden/epidemiologyABSTRACT
Background: Survivors of sepsis may experience long-term risk of increased morbidity and mortality, but estimations of cause-specific effects beyond 1 year after a sepsis episode are lacking. Method: This nationwide population-based cohort study linked data from national registers to compare patients aged ≥18 years in Sweden admitted to an intensive care unit from 2008 to 2019 with severe community-acquired sepsis. Patients were identified through the Swedish Intensive Care Registry, and randomly selected population controls were matched for age, sex, calendar year, and county of residence. Confounding from comorbidities, health care use, and socioeconomic and demographic factors was accounted for by using entropy-balancing methods. Long-term mortality and readmission rates, total and cause specific, were compared for 20 313 patients with sepsis and 396 976 controls via Cox regression. Results: During the total follow-up period, 56% of patients with sepsis died, as opposed to 26% of the weighted controls. The hazard ratio for all-cause mortality was attenuated with time but remained elevated in all periods: 3.0 (95% CI, 2.8-3.2) at 2 to 12 months after admission, 1.8 to 1.9 between 1 and 5 years, and 1.6 (95% CI, 1.5-1.8) at >5 years. The major causes of death and readmission among the sepsis cases were infectious diseases, cancer, and cardiovascular diseases. The hazard ratios were larger among those without underlying comorbidities. Conclusions: Severe community-acquired sepsis was associated with substantial long-term effects beyond 1 year, as measured by mortality and rehospitalization. The cause-specific rates indicate the importance of underlying or undetected comorbidities while suggesting that survivors of sepsis may face increased long-term mortality and morbidity not explained by underlying health factors.
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Neutrophil gelatinase-associated lipocalin (NGAL) is a novel kidney injury and inflammation biomarker. We investigated whether NGAL could be used to predict continuous renal replacement therapy (CRRT) and mortality in critical coronavirus disease 2019 (COVID-19). This prospective multicenter cohort study included adult COVID-19 patients in six intensive care units (ICUs) in Sweden between May 11, 2020 and May 10, 2021. Blood was sampled at admission, days two and seven in the ICU. The samples were batch analyzed for NGAL, creatinine, and cystatin c after the end of the study period. Initiation of CRRT and 90-day survival were used as dependent variables in regression models. Of 498 included patients, 494 were analyzed regarding CRRT and 399 were analyzed regarding survival. Seventy patients received CRRT and 154 patients did not survive past 90 days. NGAL, in combination with creatinine and cystatin c, predicted the subsequent initiation of CRRT with an area under the curve (AUC) of 0.95. For mortality, NGAL, in combination with age and sex, had an AUC of 0.83. In conclusion, NGAL is a valuable biomarker for predicting subsequent initiation of CRRT and 90-day mortality in critical COVID-19. NGAL should be considered when developing future clinical scoring systems.
Subject(s)
Acute Kidney Injury , COVID-19 , Adult , Humans , Lipocalin-2 , Cystatin C , Cohort Studies , Prospective Studies , Creatinine , Renal Dialysis , COVID-19/therapy , BiomarkersABSTRACT
BACKGROUND: Calprotectin (S100A8/A9) is a pro-inflammatory mediator primarily released from neutrophils. Previous studies have revealed associations between plasma calprotectin, disease severity and in-hospital mortality in unselected COVID-19 patients. OBJECTIVE: We aimed to assess whether plasma calprotectin dynamics during the first week of intensive care are associated with mortality and functional outcome in critically ill COVID-19 patients. METHODS: This prospective study included 498 COVID-19 patients admitted to six intensive care units (ICUs) in Sweden between May 2020 and May 2021. Blood samples were collected on ICU admission and on day 7. The primary outcome was 12-month mortality. Secondary outcomes were functional outcome of survivors at 3 and 12 months, and the need for invasive mechanical ventilation (IMV) or continuous renal replacement therapy (CRRT) during the ICU stay. Functional outcome was assessed by the Glasgow Outcome Scale Extended (GOSE, range 1-8, with < 5 representing an unfavourable outcome). Associations between plasma calprotectin and outcomes were examined in binary logistic regression analyses adjusted for age, sex, BMI, hypertension, smoking, and creatinine. RESULTS: High plasma calprotectin on admission and day 7 was independently associated with increased 12-month mortality. Increasing calprotectin from admission to day 7 was independently associated with higher mortality at 12 months [OR 2.10 (95% CI 1.18-3.74), p = 0.012], unfavourable functional outcome at 3 months [OR 2.53 (95% CI 1.07-6.10), p = 0.036], and the use of IMV [OR 2.23 (95% CI 1.10-4.53), p = 0.027)] and CRRT [OR 2.07 (95% CI 1.07-4.00), p = 0.031)]. A receiver operator characteristic (ROC) model including day 7 calprotectin and age was a good predictor of 12-month mortality [AUC 0.79 (95% CI 0.74-0.84), p < 0.001]. Day 7 calprotectin alone predicted an unfavourable functional outcome at 3 months [AUC 0.67 (95% CI 0.58-0.76), p < 0.001]. CONCLUSION: In critically ill COVID-19 patients, increasing calprotectin levels after admission to the ICU are associated with 12-month mortality and unfavourable functional outcome in survivors. Monitoring plasma calprotectin dynamics in the ICU may be considered to evaluate prognosis in critical COVID-19. STUDY REGISTRATION: ClinicalTrials.gov Identifier: NCT04974775, registered April 28, 2020.
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BACKGROUND: Bioactive adrenomedullin (bio-ADM) is a vasoactive peptide with a key role in reducing vascular hyperpermeability and improving endothelial stability during infection, but it also has vasodilatory properties. Bioactive ADM has not been studied in conjunction with acute respiratory distress syndrome (ARDS), but it has recently been shown to correlate with outcomes after severe COVID-19. Therefore, this study investigated the association between circulating bio-ADM on intensive care unit (ICU) admission and ARDS. The secondary aim was the association between bio-ADM and ARDS mortality. METHODS: We analysed bio-ADM levels and assessed the presence of ARDS in adult patients admitted to two general intensive care units in southern Sweden. Medical records were manually screened for the ARDS Berlin criteria. The association between bio-ADM levels and ARDS and mortality in ARDS patients was analysed using logistic regression and receiver-operating characteristics analysis. The primary outcome was an ARDS diagnosis within 72 h of ICU admission, and the secondary outcome was 30-day mortality. RESULTS: Out of 1224 admissions, 11% (n = 132) developed ARDS within 72 h. We found that elevated admission bio-ADM level was associated with ARDS independently of sepsis status and of organ dysfunction as measured by the Sequential organ failure assessment (SOFA) score. Both lower levels (< 38 pg/L) and high (> 90 pg/L) levels of bio-ADM were independently (of the Simplified acute physiology score, SAPS-3) predictive of mortality. Patients with indirect mechanisms of lung injury had higher bio-ADM levels than those with a direct mechanism of injury, and bio-ADM increased with increasing ARDS severity. CONCLUSIONS: High levels of bio-ADM on admission are associated with ARDS, and bio-ADM levels significantly differ depending on the injury mechanism. In contrast, both high and low levels of bio-ADM are associated with mortality, possibly due to the dual action of bio-ADM in stabilising the endothelial barrier and causing vasodilation. These findings could lead to improved diagnostic accuracy of ARDS and potentially lead to new therapeutic strategies.
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Background: Sepsis is common in the intensive care unit (ICU). Two of the ICU's most widely used mortality prediction models are the Simplified Acute Physiology Score 3 (SAPS-3) and the Sequential Organ Failure Assessment (SOFA) score. We aimed to assess the mortality prediction performance of SAPS-3 and SOFA upon ICU admission for sepsis and find a simpler mortality prediction model for these patients to be used in clinical practice and when conducting studies. Methods: A retrospective study of adult patients fulfilling the Sepsis-3 criteria admitted to four general ICUs was performed. A simple prognostic model was created using backward stepwise multivariate logistic regression. The area under the curve (AUC) of SAPS-3, SOFA and the simple model was assessed. Results: One thousand nine hundred eighty four admissions were included. A simple six-parameter model consisting of age, immunosuppression, Glasgow Coma Scale, body temperature, C-reactive protein and bilirubin had an AUC of 0.72 (95% confidence interval (CI) 0.69-0.75) for 30-day mortality, which was non-inferior to SAPS-3 (AUC 0.75, 95% CI 0.72-0.77) (p = 0.071). SOFA had an AUC of 0.67 (95% CI 0.64-0.70) and was inferior to SAPS-3 (p < 0.001) and our simple model (p = 0.0019). Conclusion: SAPS-3 has a lower prognostic value in sepsis than in the general ICU population. SOFA performs less well than SAPS-3. Our simple six-parameter model predicts mortality just as well as SAPS-3 upon ICU admission for sepsis, allowing the design of simple studies and performance monitoring.
ABSTRACT
BACKGROUND: Neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and total-tau protein (tau) are novel blood biomarkers of neurological injury, and may be used to predict outcomes in critical COVID-19. METHODS: A prospective multicentre cohort study of 117 consecutive and critically ill COVID-19 patients in six intensive care units (ICUs) in southern Sweden between May and November 2020. Serial NfL, GFAP and tau were analysed in relation to mortality, the Glasgow Outcome Scale Extended (GOSE) and the physical (PCS) and mental (MCS) components of health-related quality of life at one year. RESULTS: NfL, GFAP and tau on ICU admission predicted one-year mortality with an area under the curve (AUC) of 0.82 (95% confidence interval [CI] 0.74[Formula: see text]0.90), 0.72 (95% CI 0.62[Formula: see text]0.82) and 0.66 (95% CI 0.54[Formula: see text]0.77). NfL on admission was an independent predictor of one-year mortality (p = 0.039). Low NfL and GFAP values were associated with good PCS ([Formula: see text]45) at one year but not with good MCS ([Formula: see text]45) or GOSE ([Formula: see text]5). CONCLUSIONS: NfL on ICU admission was an independent predictor of mortality. High levels of NfL, GFAP and tau were associated with mortality but not with poor GOSE in survivors at one year. Low levels of NfL and GFAP were associated with improved physical health-related quality of life.
ABSTRACT
The unique Swecrit Biobank and its associated clinical registries for sepsis, ARDS, cardiac arrest, trauma, and COVID-19 include more than 150,000 blood samples and descriptions of critically ill patients. These assets provide a unique opportunity to research and improve the care of the most seriously ill patients through biomarker analyses, proteomic studies, and genetic and epigenetic studies using modern machine learning techniques (artificial intelligence). Interested researchers are invited to submit their proposals and participate.