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C-reactive protein (CRP) is not included in the major intensive care unit (ICU) prognostic tools such as the Simplified Acute Physiology Score (SAPS). We assessed CRP on ICU admission as a SAPS-3 independent risk marker for short-term mortality and length of stay (LOS) in ICU patients with sepsis. Adult ICU admissions satisfying the Sepsis-3 criteria to four southern Swedish hospitals were retrospectively identified and divided into a low CRP group (<100 mg/L) and a high CRP group (>100 mg/L) based on the admission CRP level. The standardized mortality ratio (SMR) was calculated. A total of 851 admissions were included. The SMR was higher in the high CRP group (0.85 vs. 0.67, P = .001 in the whole sepsis group and 0.85 vs. 0.59, P = .003 in the culture-positive subgroup). The CRP levels also correlated with ICU and hospital LOS in survivors (P < .001 and P = .002), again independent of SAPS-3. An admission CRP level >100 mg/L is associated with an increased risk of ICU and 30-day mortality as well as prolonged LOS in survivors, irrespective of morbidity measured with SAPS-3. Thus, CRP may be a simple, early marker for prognosis in ICU admissions for sepsis. •CRP is a simple way to measure the inflammatory response.•CRP is ubiquitously measured on ICU admission but not used for early scoring.•CRP for sepsis patients carries significant independent prognostic information.•The prognostic information of early CRP is independent of the SAPS 3 scoring system.

Purpose We investigated if early intensive care unit (ICU) scoring with the Simplified Acute Physiology Score (SAPS 3) could be improved using artificial neural networks (ANNs). Methods All first-time adult intensive care admissions in Sweden during 2009–2017 were included. A test set was set aside for validation. We trained ANNs with two hidden layers with random hyper-parameters and retained the best ANN, determined using cross-validation. The ANNs were constructed using the same parameters as in the SAPS 3 model. The performance was assessed with the area under the receiver operating characteristic curve (AUC) and Brier score. Results A total of 217,289 admissions were included. The developed ANN (AUC 0.89 and Brier score 0.096) was found to be superior ( p <10 −15 for AUC and p <10 −5 for Brier score) in early prediction of 30-day mortality for intensive care patients when compared with SAPS 3 (AUC 0.85 and Brier score 0.109). In addition, a simple, eight-parameter ANN model was found to perform just as well as SAPS 3, but with better calibration (AUC 0.85 and and Brier score 0.106, p <10 −5 ). Furthermore, the ANN model was superior in correcting mortality for age. Conclusion ANNs can outperform the SAPS 3 model for early prediction of 30-day mortality for intensive care patients.

COVID-19 is a severe respiratory disease affecting millions worldwide, causing significant morbidity and mortality. Adrenomedullin (bio-ADM) is a vasoactive hormone regulating the endothelial barrier and has been associated with COVID-19 mortality and other adverse events. This prospective cohort pilot study included 119 consecutive patients with verified SARS-CoV-2 infection admitted to two intensive care units (ICUs) in Southern Sweden. Bio-ADM was retrospectively analysed from plasma on ICU admission, and days 2 and 7. Information on comorbidities, adverse events and mortality was collected. The primary outcome was 90-day mortality, and secondary outcomes were markers of disease severity. The association between bio-ADM and outcomes was analysed using survival analysis and logistic regression. Bio-ADM on admission, day 2, and day 7 only moderately predicted 90-day mortality in univariate and multivariate Cox regression. The relative change in bio-ADM between sample times predicted 90-day mortality better even when adjusting for the SAPS3 score, with an HR of 1.09 (95% CI 1.04–1.15) and a C-index of 0.82 (95% CI 0.72–0.92) for relative change between day 2 and day 7. Bio-ADM had a good prediction of the need for renal replacement therapy in multivariate Cox regression adjusting for creatinine, where day 2 bio-ADM had an HR of 3.18 (95% CI 1.21–8.36) and C-index of 0.91 (95% CI 0.87–0.96). Relative changes did not perform better, possibly due to a small sample size. Admission and day 2 bio-ADM was associated with early acute kidney injury (AKI). Bio-ADM on ICU admission, day 2 and day 7 predicted 90-day mortality and dialysis needs, highlighting bio-ADM’s importance in COVID-19 pathophysiology. Bio-ADM could be used to triage patients with a risk of adverse outcomes and as a potential target for clinical interventions.

Globally, road traffic accidents (RTAs) remain a major cause of death, particularly among individuals aged 15–30 years. While Sweden has been at the forefront of traffic safety through the Vision Zero initiative, in-hospital management remains crucial in determining RTA outcomes. Drawing on North American evidence suggesting improved survival at trauma centres, the Swedish healthcare system has increasingly emphasised trauma centralisation. However, comprehensive national data from Sweden are scarce. Given the country’s unique demographic and geographic characteristics, including vast sparsely populated areas, direct comparisons with other Western systems are challenging. We analysed the epidemiology and risk factors for 30-day mortality among 95,954 RTA-related hospital admissions in Sweden between 2008 and 2021. Predictors included the ICD-based Injury Severity Score (ICISS), age, sex, Charlson Comorbidity Index (CCI), year of event, and hospital level. Mortality risk was modelled using explainable artificial intelligence (XAI) via Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP), alongside conventional multivariable logistic regression for comparison. The most influential predictors of 30-day mortality, in descending order, were ICISS, age, CCI, event year, hospital level, and sex. A clear trend toward centralisation was observed, with Level 1 hospitals admitting the most severely injured patients. However, after risk adjustment, the hospital level was not independently associated with 30-day mortality. The XAI model outperformed logistic regression in both discrimination and calibration, confirming these findings. This study represents a comprehensive national analysis of in-hospital outcomes following RTAs in Europe. ICISS, age, sex, and comorbidity influenced mortality risk, while overall survival improved over time. The assumption that trauma centralisation confers a universal survival advantage does not appear to hold in the Swedish context. These findings underscore the need to re-evaluate trauma system design under Scandinavian conditions—ensuring that timely access to hospital care is not compromised by centralisation.

Critically ill COVID-19 patients admitted to the intensive care unit (ICU) are at an increased risk of acquiring bacterial bloodstream infections (BSI). We aimed to describe patient characteristics, risk factors, and the microbiological spectrum in blood cultures and evaluate the impact of ICU-acquired BSI on outcomes in a Nordic setting. A prospective multicenter cohort study was conducted on adult invasively mechanically ventilated (IMV) COVID-19 patients. The primary aim was to identify the proportion of ICU-acquired BSI and its aetiology. Secondary outcomes were duration of IMV, length of stay (LOS), and mortality for individuals with and without BSI, respectively. Logistic regression was used to identify potential predictors of ICU-acquired BSI. Predictors were assessed by calculating an Area Under the Receiver Operating Characteristics (AUROC) curve. Of 354 included patients, 17% had an ICU-acquired BSI. Staphylococcus aureus was the most common pathogen. Patients with BSI had a longer duration of IMV (20 days versus 9 days, p < 0.001), longer ICU-LOS (24 days versus 11 days, p < 0.001), and hospital-LOS (38 days versus 24 days, p < 0.001). A BSI was associated with increased mortality; odds ratio (OR) 3.21, 95% CI: 1.61-6.38, p < 0.001. Adjusted analyses showed that higher BMI; OR 1.06, 95% CI: 1.01-1.11, p = 0.014, diabetes mellitus with organ complications; OR 2.66, 95% CI: 1.33-5.29, p = 0.005, and number of symptomatic days before ICU admission; OR 1.04, 95% CI: 1.01-1.07, p = 0.008, were associated with a BSI. The AUROC was 0.66 (95% CI: 0.58-0.74). ICU-acquired BSIs were found in 17% of critically ill COVID-19 patients and were associated with a longer duration of IMV and LOS as well as increased mortality. Staphylococcus aureus was the dominating pathogen. We found several factors associated with ICU-acquired BSIs at ICU admission. However, their ability to predict BSIs was poor.

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.

Diagnosing sepsis in critical care remains a challenge due to the lack of gold-standard diagnostics. Calprotectin (S100A8/A9) has been proposed as a diagnostic marker to identify sepsis in critically ill patients. This study evaluated the diagnostic performance of calprotectin and C-reactive protein (CRP) to distinguish between sepsis and non-sepsis on intensive care unit (ICU) admission. Admission biobank blood samples from adult patients admitted to four ICUs (2015–2018) were used to analyse calprotectin and CRP. All adult patients were screened retrospectively for the sepsis-3 criteria at ICU admission. The diagnostic performance of calprotectin and CRP was evaluated using receiver operating characteristic (ROC) curves. We included 4732 patients, of whom 44% had sepsis. Calprotectin levels were higher in sepsis ( p  < 0.001). The area under the receiver operating curve (AUROC) to diagnose sepsis was 0.61 for calprotectin compared to 0.72 for CRP ( p  < 0.001). Among microbiological subgroups of sepsis patients, fungal sepsis had the highest level of calprotectin. We conclude that the diagnostic performance of calprotectin in identifying sepsis patients at ICU admission was inferior to that of CRP.

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.

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.