Clinical review: What is the role for autopsy in the ICU?

The availability of advanced diagnostic tools has grown in the past decades. Hence, a growing false belief exists that everything is known about the patient before death. Moreover, intensivists may wrongly believe that autopsy findings do not contribute to the understanding of pathophysiological events. The immediate result is that few ICUs nowadays assemble enough autopsy cases with new and interesting clinicopathological features. However, we believe that, at least in tertiary ICUs, autopsies remain a valuable examination, as a tool for quality control, as a way of establishing gold standards for diagnostic examinations and as an aid in developing guidelines for treatment and diagnosis of diseases frequently encountered in the ICU. Finally, due to the ever-expanding armamentarium of immunosuppressive agents, a growing list of opportunistic infections is discovered during autopsy. The present article gives an overview of autopsy studies conducted in the ICU and discusses the pros and cons of performing these

Cytomegalovirus: A Troll in the ICU? Overview of the Literature and Perspectives for the Future

Cytomegalovirus (CMV) is one of the most pathogenic viruses in human. After a primary infection, CMV resides in the host for life as a latent infection. When immunity is reduced, CMV can escape the suppressive effects of the immune system and lead to viremia and antigenemia. This reactivation, first seen in transplant patients, has also been documented in non-immunocompromised CMV-seropositive critically ill patients and is associated with higher morbidity and mortality. In the latter, it is not clear whether CMV reactivation is an innocent bystander or the cause of this observed worse outcome. Two studies showed no difference in the outcome of CMV-seropositive and seronegative patients. In addition, proof-of-concept studies investigating prophylactic antiviral treatment to prevent CMV reactivation during critical illness, failed to show a beneficial effect on interleukin levels or clinical outcome. Further research is necessary to resolve the question whether CMV replication impairs the prognosis in non-immunocompromised critically ill patients. We here give a concise overview on the available data and propose strategies to further unravel this question. First, post-mortem investigation may be useful to evaluate the effect of viral replication on organ inflammation and function. Second, further research should focus on the question whether the level of viremia needs to exceed a threshold to be associated with worse outcome. Third, clinical and biochemical assessments may help to identify patients at high risk for reactivation. Fourth, preemptive treatment based upon early detection of the virus is currently under investigation. Finally, immune-stimulating biologicals may be beneficial in high-risk groups.status: publishe

Cytomegalovirus in Patients in the Intensive Care Unit

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Artificial intelligence to guide management of acute kidney injury in the ICU: a narrative review

PURPOSE OF REVIEW: Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associated with high morbidity and mortality. The risk of developing AKI depends on the presence of preexisting comorbidities and the cause of the current disease. Besides, many other parameters affect the kidney function, such as the state of other vital organs, the host response, and the initiated treatment. Advancements in the field of informatics have led to the opportunity to store and utilize the patient-related data to train and validate models to detect specific patterns and, as such, predict disease states or outcomes. RECENT FINDINGS: Machine-learning techniques have also been applied to predict AKI, as well as the patients' outcomes related to their AKI, such as mortality or the need for kidney replacement therapy. Several models have recently been developed, but only a few of them have been validated in external cohorts. SUMMARY: In this article, we provide an overview of the machine-learning prediction models for AKI and its outcomes in critically ill patients and individuals undergoing major surgery. We also discuss the pitfalls and the opportunities related to the implementation of these models in clinical practices.status: publishe

The soluble mannose receptor (sMR/sCD206) in critically ill patients with invasive fungal infections, bacterial infections or non-infectious inflammation: a secondary analysis of the EPaNIC RCT

BACKGROUND: Invasive fungal infections (IFI) are difficult to diagnose, especially in critically ill patients. As the mannose receptor (MR) is shed from macrophage cell surfaces after exposure to fungi, we investigate whether its soluble serum form (sMR) can serve as a biomarker of IFI. METHODS: This is a secondary analysis of the multicentre randomised controlled trial (EPaNIC, n = 4640) that investigated the impact of initiating supplemental parenteral nutrition (PN) early during critical illness (Early-PN) as compared to withholding it in the first week of intensive care (Late-PN). Serum sMR concentrations were measured in three matched patient groups (proven/probable IFI, n = 82; bacterial infection, n = 80; non-infectious inflammation, n = 77) on the day of antimicrobial initiation or matched intensive care unit day and the five preceding days, as well as in matched healthy controls (n = 59). Independent determinants of sMR concentration were identified via multivariable linear regression. Serum sMR time profiles were analysed with repeated-measures ANOVA. Predictive properties were assessed via area under the receiver operating curve (aROC). RESULTS: Serum sMR was higher in IFI patients than in all other groups (all p < 0.02), aROC to differentiate IFI from no IFI being 0.65 (p < 0.001). The ability of serum sMR to discriminate infectious from non-infectious inflammation was better with an aROC of 0.68 (p < 0.001). The sMR concentrations were already elevated up to 5 days before antimicrobial initiation and remained stable over time. Multivariable linear regression analysis showed that an infection or an IFI, higher severity of illness and sepsis upon admission were associated with higher sMR levels; urgent admission and Late-PN were independently associated with lower sMR concentrations. CONCLUSION: Serum sMR concentrations were higher in critically ill patients with IFI than in those with a bacterial infection or with non-infectious inflammation. However, test properties were insufficient for diagnostic purposes.status: publishe

External Validation of the Augmented Renal Clearance Predictor in Critically Ill COVID-19 Patients

The ARC predictor is a prediction model for augmented renal clearance (ARC) on the next intensive care unit (ICU) day that showed good performance in a general ICU setting. In this study, we performed a retrospective external validation of the ARC predictor in critically ill coronavirus disease 19 (COVID-19) patients admitted to the ICU of the University Hospitals Leuven from February 2020 to January 2021. All patient-days that had serum creatinine levels available and measured creatinine clearance on the next ICU day were enrolled. The performance of the ARC predictor was evaluated using discrimination, calibration, and decision curves. A total of 120 patients (1064 patient-days) were included, and ARC was found in 57 (47.5%) patients, corresponding to 246 (23.1%) patient-days. The ARC predictor demonstrated good discrimination and calibration (AUROC of 0.86, calibration slope of 1.18, and calibration-in-the-large of 0.14) and a wide clinical-usefulness range. At the default classification threshold of 20% in the original study, the sensitivity and specificity were 72% and 81%, respectively. The ARC predictor is able to accurately predict ARC in critically ill COVID-19 patients. These results support the potential of the ARC predictor to optimize renally cleared drug dosages in this specific ICU population. Investigation of dosing regimen improvement was not included in this study and remains a challenge for future studies

Development and validation of the creatinine clearance predictor machine learning models in critically ill adults

Abstract Background In critically ill patients, measured creatinine clearance (CrCl) is the most reliable method to evaluate glomerular filtration rate in routine clinical practice and may vary subsequently on a day-to-day basis. We developed and externally validated models to predict CrCl one day ahead and compared them with a reference reflecting current clinical practice. Methods A gradient boosting method (GBM) machine-learning algorithm was used to develop the models on data from 2825 patients from the EPaNIC multicenter randomized controlled trial database. We externally validated the models on 9576 patients from the University Hospitals Leuven, included in the M@tric database. Three models were developed: a “Core” model based on demographic, admission diagnosis, and daily laboratory results; a “Core + BGA” model adding blood gas analysis results; and a “Core + BGA + Monitoring” model also including high-resolution monitoring data. Model performance was evaluated against the actual CrCl by mean absolute error (MAE) and root-mean-square error (RMSE). Results All three developed models showed smaller prediction errors than the reference. Assuming the same CrCl of the day of prediction showed 20.6 (95% CI 20.3–20.9) ml/min MAE and 40.1 (95% CI 37.9–42.3) ml/min RMSE in the external validation cohort, while the developed model having the smallest RMSE (the Core + BGA + Monitoring model) had 18.1 (95% CI 17.9–18.3) ml/min MAE and 28.9 (95% CI 28–29.7) ml/min RMSE. Conclusions Prediction models based on routinely collected clinical data in the ICU were able to accurately predict next-day CrCl. These models could be useful for hydrophilic drug dosage adjustment or stratification of patients at risk. Trial registration. Not applicable