Retrospective analysis of data collection of ventilator settings and ventilator-derived parameters with comparison of autonomous computer led data recording and nurse led paper flow sheet recording in Intensive Care

Introduction: Increased use of technology in Intensive Care Medicine has become integral to the specialty over the last 50 years (1) This, accompanied by advances in medical practice, has led to larger quantities of data amongst ventilated patients and a need to accurately record ventilator parameters to make clinical decisions (2) It is important to evaluate traditional paper-based data entry with new automated data capture in intensive care units to ensure continued best practice This audit aims to assess data collection failures in ventilator setting recordings amongst intubated and ventilated COVID-19 patients and directly contrast failures of hourly recording amongst the ICCA system and data entered in paper ICU flow sheets We analysed 13,355 data entries over a one-week period in our ICU to compare recording failure of computer-based records and traditional flow sheet entry Objectives: The objective of this audit is to directly compare and analyse the performance of paper-based nurse led data entry against computer based patient recording Primary end points were hourly recordings of the following values: FiO2, SpO2, EtCO2, Respiratory Rate, Ventilator Mode, Tidal Volumes, Minute Volume, Peak Airway Pressure and Positive End Expiratory Pressure over a one-week period By analysing the results, we aim to gain insight into the benefits and potential failures of automated computing in data recording as its use is further incorporated into clinical practice Methods: Hourly ventilator data record entry was retrospectively analysed for intubated patients from the 13th -19th of April in a Dublin teaching hospital ICU The IT system and patient flow sheet for each patient were analysed All data was available for inclusion Patients who were extubated during the week were included until the hour of extubation Hours when a result was not available for FiO2, SpO2, EtcO2, Respiratory Rate, Ventilator Mode, Tidal Volumes, Minute Volume, Peak Airway Pressure and PEEP were recorded These 13,355 data points were separated by patient hours when either the ICCA system or Nurse led patient flow sheet was used with 9270 patient hours in the ICCA arm and 4283 patient hours in the Paper recording arm, further analysis was based upon time of day, (day shift 0800-2000 and night shift 2000-0800) These data were then entered onto a database for analysis Results: 13,355 data entries were collated Within the IT based ICCA system 4% (368 hours of 9270 hours) of data entries were unrecorded compared to paper flow sheet entries 7 6%, (487 hours of 4283 hours data entries) Of ICCA recordings that were missed 62 5% were missed during day shifts, (0800-2000) and 37 5% during night shifts, (2000- 0800) This contrasts with paper flow sheet entry in which 39 5% of data entries missed during day shifts and 60 5% during night shifts Results were also separated by parameter missed as percentage of total recorded hours The ICCA system resulted in: End Tidal (2%), Peak Airway Pressure (0 728%), Minute Volume (0 65%), Tidal Volume (0 287%), SpO2 (0 12%), PEEP (0 12%), FiO2 (0 077%), Ventilator Mode (0 066%) Respiratory Rate (0%) Percentages refer to hours missed as a proportion of total hours Paper flow sheets recording resulted in: End Tidal CO2(2 1%), SpO2 (1 5%), Ventilator Mode (1 42%), FiO2 (1 12%), PEEP (1%), Minute Volume (0 21%) Peak Airway Pressure (0 163%), Tidal Volume (0 14%), Respiratory Rate (0 02%) Of note a range of data entry failures occurred across multiple parameters simultaneously This was consistent with patient movement for diagnostic tests and procedures and was not removed from the data set EtCO2 performed poorly in both groups It is hypothesised that air temperature and humidity change within the unit during the COVID- 19 epidemic contributed in part to these results Conclusion: Accurate data collection provides clinicians with the ability to make informed decisions on patient care Our study concludes that ventilation parameters are recorded more accurately with the use of digital computer systems when total data failures are compared to total patient hours, (4% and 7 6%) The improved performance is even more clear if EtCO2 is removed as it results in 2 1% of 4% of the ICCA computing failures While this confers more accurate data available for clinicians there were parameters where paper flow sheets had improved recording over automated data entry These include peak airway pressure, tidal volume and minute ventilation while ICCA had improved performance in FiO2, SpO2, ventilator setting and PEEP EtCO2 had the worst recording rate amongst both modalities We can conclude that the use of automated computing provides superior overall recording rates, however, nurse led paper flow sheets had improved recording in some parameters and recorded less proportion of data misses during daylight hours