Predicting dead space ventilation in critically ill patients using clinically available data

David Frankenfield, Shoaib Alam, Edgar Bekteshi, Robert Vender

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


OBJECTIVE: To develop and validate an equation to predict dead space to tidal volume ratio (Vd/Vt) from clinically available data in critically ill mechanically ventilated patients. DESIGN: Prospective, observational study using a convenience sample of patients whose arterial blood gas and respiratory gas exchange had been measured with indirect calorimetry. SETTING: Medical and surgical critical care units of a university medical center. PATIENTS: Adult, mechanically ventilated patients at rest with Fio2 ≤0.60 and no air leaks who had recent arterial blood gas recordings and end-tidal carbon dioxide concentration monitoring. INTERVENTIONS: Observational only. MEASUREMENTS AND MAIN RESULTS: Indirect calorimetry was used to determine carbon dioxide production and expired minute ventilation in 135 patients. Tidal volume and respiratory rate were recorded from the ventilator. End tidal carbon dioxide concentration, body temperature, arterial carbon dioxide partial pressure (Paco2), and other clinical data were recorded. Vd/Vt was calculated using the Enghoff modification of the Bohr equation (Paco2-PECO2/Paco2). Regression analysis was then used to construct a predictive equation for Vd/Vt using the clinical data: Vd/Vt = 0.32 + 0.0106 (Paco2-ETCO2) + 0.003 (RR) + 0.0015 (age) (R = 0.67). A second group of 50 patients was measured using the same protocol and their data were used to validate the equations developed from the original 135 patients. The equation was found to be unbiased and precise. CONCLUSIONS: Vd/Vt is predictable from clinically available data. Whether this predicted quantity is valuable clinically must still be determined.

Original languageEnglish (US)
Pages (from-to)288-291
Number of pages4
JournalCritical care medicine
Issue number1
StatePublished - Jan 2010

All Science Journal Classification (ASJC) codes

  • Critical Care and Intensive Care Medicine


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