Prediction equation to estimate dead space to tidal volume fraction correlates with mortality in critically ill patients

Redefining ARDS: a paradigm shift

Although the defining elements of "acute respiratory distress syndrome" (ARDS) have been known for over a century, the syndrome was first described in 1967. Since then, despite several revisions of its conceptual definition, it remains a matter of debate whether ARDS is a discrete nosological entity. After almost 60 years, it is appropriate to examine how critical care has modeled this fascinating syndrome and affected patient's outcome. Given that the diagnostic criteria of ARDS (e.g., increased pulmonary vascular permeability and diffuse alveolar damage) are difficult to ascertain in clinical practice, we believe that a step forward would be to standardize the assessment of pulmonary and extrapulmonary involvement in ARDS to ensure that each patient can receive the most appropriate and effective treatment. The selection of treatments based on arbitrary ranges of PaO2/FiO2 lacks sufficient sensitivity to individualize patient care.

Comparison of the pulmonary dead-space fraction derived from ventilator volumetric capnography and a validated equation in the survival prediction of patients with acute respiratory distress syndrome

PURPOSE

This prospective observational study aims to evaluate the accuracy of dead-space fraction derived from the ventilator volumetric capnography (volumetric CO₂) or a prediction equation to predict the survival of mechanically ventilated patients with acute respiratory distress syndrome (ARDS).

METHODS

Consecutive VD/VT measurements were obtained based upon a prediction equation validated by Frankenfield et al for dead-space ventilation fraction: VD/VT = 0.320 + 0.0106 (PaCO₂-ETCO₂)⁺ 0.003 (RR)⁺0.0015 (age) in adult patients who had infection-related severe pneumonia and were confirmed as having ARDS. Here PaCO₂ is the arterial partial pressure of carbon dioxide in mmHg; ETCO₂, the end- tidal carbon dioxide measurement in mmHg; RR, respiratory rate per minute; and age in years. Once the patient had intubation, positive end expiratory pressure was adjusted and after Phigh reached a steady state, VD/VT was measured and recorded as the data for the first day. VD/VT measurement was repeated on days 2, 3, 4, 5 and 6. Meanwhile we collected dead-space fraction directly from the ventilator volu- metric CO₂ and recorded it as Vd/Vt. We analyzed the changes in VD/VT and Vd/Vt over the 6-day period to determine their accuracy in predicting the survival of ARDS patients.

RESULTS

Overall, 46 patients with ARDS met the inclusion criteria and 24 of them died. During the first 6 days of intubation, VD/VT was significantly higher in nonsurvivors on day 4 (0.70 ± 0.01 vs 0.57 ± 0.01), day 5 (0.73 ± 0.01 vs. 0.54 ± 0.01), and day 6 (0.73 ± 0.02 vs. 0.54 ± 0.01) (all p =0.000). Vd/Vt showed no significant difference on days 1e4 but it was much higher in nonsurvivors on day 5 (0.45 ± 0.04 vs. 0.41 ± 0.06) and day 6 (0.47 ± 0.05 vs. 0.40 ± 0.03) (both p=0.008). VD/VT on the fourth day was more accurate to predict survival than Vd/Vt. The area under the receiver-operating characteristic curve for VD/VT and Vd/Vt in evaluating ARDS patients survival was day 4 (0.974 ± 0.093 vs. 0.701 ± 0.023, p = 0.0024) with the 95% confidence interval being 0.857-0.999 vs. 0.525-0.841.

CONCLUSION

Compared with Vd/Vt derived from ventilator volumetric CO₂, VD/VT on day 4 calculated by Frankenfield et al's equation can more accurately predict the survival of ARDS patients.

Critical care for paediatric patients with heart failure

This review offers a critical-care perspective on the pathophysiology, monitoring, and management of acute heart failure syndromes in children. An in-depth understanding of the cardiovascular physiological disturbances in this population of patients is essential to correctly interpret clinical signs, symptoms and monitoring data, and to implement appropriate therapies. In this regard, the myocardial force-velocity relationship, the Frank-Starling mechanism, and pressure-volume loops are discussed. A variety of monitoring modalities are used to provide insight into the haemodynamic state, clinical trajectory, and response to treatment. Critical-care treatment of acute heart failure is based on the fundamental principles of optimising the delivery of oxygen and minimising metabolic demands. The former may be achieved by optimising systemic arterial oxygen content and the variables that determine cardiac output: heart rate and rhythm, preload, afterload, and contractility. Metabolic demands may be decreased by a number of ways including positive pressure ventilation, temperature control, and sedation. Mechanical circulatory support should be considered for refractory cases. In the near future, monitoring modalities may be improved by the capture and analysis of complex clinical data such as pressure waveforms and heart rate variability. Using predictive modelling and streaming analytics, these data may then be used to develop automated, real-time clinical decision support tools. Given the barriers to conducting multi-centre trials in this population of patients, the thoughtful analysis of data from multi-centre clinical registries and administrative databases will also likely have an impact on clinical practice.