Pneumonia and acute respiratory distress syndrome are common and important causes of respiratory failure in the intensive care unit with a significant impact on morbidity, mortality and health care utilization despite early antimicrobial therapy and lung protective mechanical ventilation. Both clinical entities are characterized by acute pulmonary inflammation in response to direct or indirect lung injury. Adjunct anti-inflammatory treatment with corticosteroids is increasingly used, although the evidence for benefit is limited. The treatment decisions are based on radiographic, clinical and physiological variables without regards to inflammatory state. Current evidence suggests a role of biomarkers for the assessment of severity, and distinguishing sub-phenotypes (hyperinflammatory versus hypo-inflammatory) with important prognostic and therapeutic implications. Although many inflammatory biomarkers have been studied the most common and of interest are C-reactive protein, procalcitonin, and pro-inflammatory cytokines including interleukin 6. While extensively studied as prognostic tools (prognostic enrichment), limited data are available for the role of biomarkers in determining appropriate initiation, timing and dosing of adjunct anti-inflammatory treatment (predictive enrichment)

The use of electronic cigarettes, known as vaping, has become increasingly popular over the past decade, particularly in the adolescent and young adult population, often exposing users to harmful chemicals. Vaping has been associated with a heterogeneous group of pulmonary disease. Recently, a multistate epidemic has emerged surrounding vaping-related acute lung injury, prompting the Centers for Disease Control and Prevention to list an official health advisory. In this review, we describe the current literature on the epidemiology, clinical significance, as well as recommended evaluation and treatment of vaping-related lung injury.

Objectives: To develop and verify a digital twin model of critically ill patient using the causal artificial intelligence approach to predict the response to specific treatment during the first 24 hours of sepsis. Design: Directed acyclic graphs were used to define explicitly the causal relationship among organ systems and specific treatments used. A hybrid approach of agent-based modeling, discrete-event simulation, and Bayesian network was used to simulate treatment effect across multiple stages and interactions of major organ systems (cardiovascular, neurologic, renal, respiratory, gastrointestinal, inflammatory, and hematology). Organ systems were visualized using relevant clinical markers. The application was iteratively revised and debugged by clinical experts and engineers. Agreement statistics was used to test the performance of the model by comparing the observed patient response versus the expected response (primary and secondary) predicted by digital twin. Setting: Medical ICU of a large quaternary- care academic medical center in the United States. Patients or Subjects: Adult (> 18 year yr old), medical ICU patients were included in the study. Interventions: No additional interventions were made beyond the standard of care for this study. Measurements and Main Results: During the verification phase, model performance was prospectively tested on 145 observations in a convenience sample of 29 patients. Median age was 60 years (54–66 d) with a median Sequential Organ Failure Assessment score of 9.5 (interquartile range, 5.0–14.0). The most common source of sepsis was pneumonia, followed by hepatobiliary. The observations were made during the first 24 hours of the ICU admission with one-step interventions, comparing the output in the digital twin with the real patient response. The agreement between the observed versus and the expected response ranged from fair (kappa coefficient of 0.41) for primary response to good (kappa coefficient of 0.65) for secondary response to the intervention. The most common error detected was coding error in 50 observations (35%), followed by expert rule error in 29 observations (20%) and timing error in seven observations (5%). Conclusions: We confirmed the feasibility of development and prospective testing of causal artificial intelligence model to predict the response to treatment in early stages of critical illness. The availability of qualitative and quantitative data and a relatively short turnaround time makes the ICU an ideal environment for development and testing of digital twin patient models. An accurate digital twin model will allow the effect of an intervention to be tested in a virtual environment prior to use on real patients.

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Pneumonia and acute respiratory distress syndrome are common and important causes of respiratory failure in the intensive care unit with a significant impact on morbidity, mortality and health care utilization despite early antimicrobial therapy and lung protective mechanical ventilation. Both clinical entities are characterized by acute pulmonary inflammation in response to direct or indirect lung injury. Adjunct anti-inflammatory treatment with corticosteroids is increasingly used, although the evidence for benefit is limited. The treatment decisions are based on radiographic, clinical and physiological variables without regards to inflammatory state. Current evidence suggests a role of biomarkers for the assessment of severity, and distinguishing sub-phenotypes (hyper-inflammatory versus hypo-inflammatory) with important prognostic and therapeutic implications. Although many inflammatory biomarkers have been studied the most common and of interest are C-reactive protein, procalcitonin, and pro-inflammatory cytokines including interleukin 6. While extensively studied as prognostic tools (prognostic enrichment), limited data are available for the role of biomarkers in determining appropriate initiation, timing and dosing of adjunct anti-inflammatory treatment (predictive enrichment)