Development and validation of a prediction model for persistent functional impairment among older ICU survivors

Recovery from Sepsis: Management beyond Acute Care

Recovery from sepsis is a key global health issue, impacting 38 million sepsis survivors worldwide per year. Sepsis survivors face a wide range of physical, cognitive, and psychosocial sequelae. Readmissions to hospital following sepsis are an important driver of global healthcare utilization and cost. Family members of sepsis survivors also experience significant stressors related to their role as informal caregivers. Increasing recognition of the burdens of sepsis survivorship has led to the development of postsepsis recovery programs to better support survivors and their families, although optimal models of care remain uncertain. The goal of this article is to perform a narrative review of recovery from sepsis from the perspective of patients, families, and health systems.

Prognosticating the outcome of intensive care in older patients-a narrative review

Prognosis determines major decisions regarding treatment for critically ill patients. Statistical models have been developed to predict the probability of survival and other outcomes of intensive care. Although they were trained on the characteristics of large patient cohorts, they often do not represent very old patients (age ≥ 80 years) appropriately. Moreover, the heterogeneity within this particular group impairs the utility of statistical predictions for informing decision-making in very old individuals. In addition to these methodological problems, the diversity of cultural attitudes, available resources as well as variations of legal and professional norms limit the generalisability of prediction models, especially in patients with complex multi-morbidity and pre-existing functional impairments. Thus, current approaches to prognosticating outcomes in very old patients are imperfect and can generate substantial uncertainty about optimal trajectories of critical care in the individual. This article presents the state of the art and new approaches to predicting outcomes of intensive care for these patients. Special emphasis has been given to the integration of predictions into the decision-making for individual patients. This requires quantification of prognostic uncertainty and a careful alignment of decisions with the preferences of patients, who might prioritise functional outcomes over survival. Since the performance of outcome predictions for the individual patient may improve over time, time-limited trials in intensive care may be an appropriate way to increase the confidence in decisions about life-sustaining treatment.

Development and validation of early prediction models for new-onset functional impairment in patients after being transferred from the ICU

A significant number of intensive care unit (ICU) survivors experience new-onset functional impairments that impede their activities of daily living (ADL). Currently, no effective assessment tools are available to identify these high-risk patients. This study aims to develop an interpretable machine learning (ML) model for predicting the onset of functional impairment in critically ill patients. Data for this study were sourced from a comprehensive hospital in China, focusing on adult patients admitted to the ICU from August 2022 to August 2023 without prior functional impairments. A least absolute shrinkage and selection operator (LASSO) model was utilized to select predictors for inclusion in the model. Four models, logistic regression, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were constructed and validated. Model performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Additionally, the DALEX package was employed to enhance the interpretability of the final models. The study ultimately included 1,380 patients, with 684 (49.6%) exhibiting new-onset functional impairment on the seventh day after leaving the ICU. Among the four models evaluated, the SVM model demonstrated the best performance, with an AUC of 0.909, accuracy of 0.838, sensitivity of 0.902, specificity of 0.772, PPV of 0.802, and NPV of 0.886. ML models are reliable tools for predicting new-onset functional impairments in critically ill patients. Notably, the SVM model emerged as the most effective, enabling early identification of patients at high risk and facilitating the implementation of timely interventions to improve ADL.

Prediction of Post-ICU Impairments-Is It Possible?

Mr Thompson is a 58-year-old male, admitted to the intensive care unit with severe community acquired pneumonia following an overseas holiday. He has acute respiratory failure, requiring mechanical ventilation and haemodynamic supports. Prior to his ICU admission he worked in a corporate job, lived with his wife, was otherwise fit and healthy, and enjoyed cycling 10km to and from work. His ICU admission extended for three weeks, and he stayed in hospital for a further two weeks, before discharging. On return home, he is only able to walk 200m. He has lost his vitality. He is tearful, anxious, and depressed about his health state. His wife is finding it difficult to help him. They are unsure of where to get support in the community, whether he will be able to return to work, and what to expect for his recovery.