Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a “precision medicine” approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Introduction: Assessment of fluid status in critically ill patients is challenging. We aimed to assess the feasibility and validity of bioelectrical impedance vector analysis (BIVA) as a measure of hydration in critically ill patients. Methods: We performed twice-daily BIVA measurements and fluid balance calculations and recorded physiological variables in mechanically ventilated patients within 24 h of intensive care unit (ICU) admission for up to 5 days. Treating clinicians were blinded to BIVA results. Results: We performed 344 BIVA measurements in 61 patients. According to BIVA, 14 patients (23 %) were dehydrated, 22 (36 %) were normally hydrated and 25 (41 %) were overhydrated upon ICU admission. Patients with normal BIVA hydration were less sick, had fewer comorbidities and had less deranged physiology than patients found to be dehydrated or overhydrated with BIVA. Cumulative fluid balance increased in patients found to be dehydrated with BIVA by a mean of 3.4±2.2 L, whereas in patients found to be overhydrated with BIVA, it decreased by a mean of 4.5±6.9 L. In patients found to be normally hydrated with BIVA, fluid balance remained unchanged. BIVA-defined hydration increased with 1 L (median change 1.5 %, P =0.09) or 2 L (median change 0.7 %, P =0.09) of calculated fluid gains. BIVA-defined hydration decreased (median change −0.8 %, P =0.02) with a negative cumulative fluid balance of >2 L. BIVA-defined hydration between first and last measurement correlated with the corresponding change in fluid balance (ρ =0.25, P =0.05). Conclusions: BIVA is feasible in critically ill patients. Its validity is supported by the observed characteristics of patients with different degrees of BIVA hydration upon admission and by different fluid management of such patients by blinded clinicians. The sensitivity of repeated BIVA hydration measurements to detect fluid accumulation or fluid balance changes <2 L was low, however. These contradictory findings provide the rational basis for studies of BIVA-assisted fluid management in ICU patients.

Introduction: Post-intensive care syndrome (PICS) is an emerging problem in critically ill patients and the prevalence and risk factors are unclear in patients with severe coronavirus disease 2019 (COVID-19). This multicenter prospective observational study aimed to investigate the prevalence and risk factors of PICS in ventilated patients with COVID-19 after ICU discharge. Methods: Questionnaires were administered twice in surviving patients with COVID-19 who had required mechanical ventilation, concerning Barthel Index, Short-Memory Questionnaire, and Hospital Anxiety and Depression Scale scores. The risk factors for PICS were examined using a multivariate logistic regression analysis. Results: The first and second PICS surveys were obtained at 5.5 and 13.5 months (mean) after ICU discharge, with 251 and 209 patients completing the questionnaires and with a prevalence of PICS of 58.6% and 60.8%, respectively, along with the highest percentages of cognitive impairment. Delirium (with an odds ratio of (OR) 2.34, 95% CI 1.1–4.9, and p = 0.03) and the duration of mechanical ventilation (with an OR of 1.29, 95% CI 1.05–1.58, and p = 0.02) were independently identified as the risk factors for PICS in the first PICS survey. Conclusion: Approximately 60% of the ventilated patients with COVID-19 experienced persistent PICS, especially delirium, and required longer mechanical ventilation.