Background: Pennsylvania Respiratory Research Collaborative (PRRC) convened a sub-group to study quality metrics monitored by respiratory departments in Pennsylvania healthcare facilities. The aim was to understand quality metrics tracked by and involving respiratory care departments as the first step in developing state-wide quality benchmarks. Methods: A survey focusing on quality metrics monitored by respiratory care departments was developed and sent to supervisors, managers, and directors within the state of Pennsylvania between April 25–May 31, 2021. In collaboration with PSRC Executive Director and Board, the survey committee provided the survey link via email to the PSRC listserv consisting of respiratory care leaders. Reminder emails were sent two weeks after the initial email invite. Respondents were asked not to complete the survey more than once. The survey was anonymous and received IRB exempt approval. Results: One hundred and ten leaders received the survey with a response rate of 64.54% (71/110). Of these respondents, 88.73% participate in a quality improvement process that includes respiratory care in multidisciplinary initiatives. Centers by hospital category included academic teaching facility (38.03%), community hospital (52.11%), acute rehabilitation (5.63%), critical access hospital (1.40%), and cancer specialty (1.40%). Distribution of hospital size included 600 beds (9.86%). Most common quality measures were ventilator-associated infections, medication barcode scanning, and COPD/PNA readmissions (Table 1). Quality metrics were shared with respiratory care staff most commonly during staff meetings (Figure 1). In addition to departmental information sharing, metrics are also reported to hospital administration as departmental report outs, operations, quality, and safety committees. The following metrics were listed the most number of times to be reported state-wide among PA respiratory care departments: ventilator-associated conditions, COPD readmission rates, unplanned extubations, and ventilator LOS. Conclusions: A large number of respondents are tracking similar metrics and reporting them internally. A statewide or nationwide reporting and benchmarking system managed by respiratory care will benefit Respiratory Departments as well as illustrating value of the profession. More study is needed to identify whether these findings are consistent throughout the country.

Background: Patients that wear non-invasive ventilation devices at home for chronic respiratory failure are admitted the hospital and continue non-invasive therapy as an inpatient in the acute care setting. These patients sometimes require oxygen. Per the user manual, oxygen may be added at either the mask or device. This study aimed to find optimal placement of the oxygen bleed-in adaptor. We wanted to find out which place gave the highest delivered amount of oxygen in the most consistent manner. Methods: We used a DreamStation BiPAP machine (Respironics Inc., Murrysville, PA) with the standard 15mm circuit connected to a Michigan Instruments (Kentwood, MI) test lung via mannequin head with a small full face mask. A V500 ventilator (Drager Medical, Lubeck, Germany) was used to simulate breathing. FIO2 was measured via an oxygen analyzer placed into the top port of the test lung. Continuous positive airway pressure (CPAP) and bilevel positive airway pressure (BiPAP) modes were tested. CPAP pressures were set at common end positive airway pressures (EPAP) of 5, 8, 10, 12, 15, and 20 cm H2O. The same EPAP levels were used with BiPAP with the inspiratory positive airway pressure (IPAP) was set at 5 cm H2O above EPAP. The oxygen adaptor was placed in-line at the mask and the device for all pressure settings. Oxygen flow was set in 2 L/min increments from 2 – 12 L/min for two minutes or until equilibration occurred. Flow was turned off and the next test was not started until the oxygen analyzer returned to room air. Results: All data was analyzed through IBM SPSS software v25 (IBM, Armonk, New York). The mean change of FIO2 (%) for every 2 L/min increase while on CPAP averaged 2.84 ± 0.55 at the mask and 3.73 ± 0.41 at the device. The mean change of FIO2 (%) for every 2 L/min increase while on BiPAP averaged 5.78 ± 2.95 at the mask and 5.73 ± 0.96 at the device. Changes in FIO2 were significant when on CPAP and when the oxygen adaptor was at the device, P = .01. There were no significant changes (P = .97) in FIO2 between oxygen adaptor placement when the mode was BiPAP. Conclusions: Placement of the oxygen adaptor at the device appears to give a higher FIO2 with CPAP. FIO2 on BiPAP is higher when the oxygen adaptor is at the mask, however it is not significant. Oxygen placement at the device does offer less variation on both modes. The results of this study were based on one device; further studies could be done to confirm findings.

Background: Measurement of arm span has been used as a predictor of height in pulmonary function testing and ulnar length measurement is used by nutritional experts. Can these measurements be used to predict height in patients that are admitted inpatient that do not have a documented standing height? We sought to find correlation between measured height standing and the measurement of arm span and measured ulnar lengths. Methods: IRB approval was obtained for this prospective study of patients receiving outpatient pulmonary function testing (PFT). Patients presenting to the pulmonary physiology lab for PFT’s that could stand to have accurate height measured were included. Exclusion selection criteria included those that were unable to stand for measurements, or those that did not consent. The respiratory therapist would measure the patient’s height standing, followed by arm span, and the left and right ulnar lengths. Age, sex, race, and perceived conditions that could affect measurements were also recorded. Results: 601 patients were recruited with 50 being excluded during data analysis. Data was analyzed using IBM SPSS software v25 (IBM, Armonk, New York). The measurements were as follows: mean height standing was 166.53 ± 9.97 cm, mean arm span was 171.08 ± 12.57 cm, predicted height based on ulnar length was 170.79 ± 9.15 cm for the right arm and 171.02 ± 9.36 cm for the left arm. Bivariate correlation was used to compare height standing to arm span and predicted height from ulnar length for the right and left arms which produced Pearson coefficients of 0.851, 0.759 and 0.752 respectively and all significant (P Conclusions: Arm span measurements strongly correlated to measured height while ulnar length produced a moderate correlation. In the absence of measured height standing; arm span and ulnar length may be viable to predict a patient’s height.

Background: A person is considered overweight when their body mass index (BMI) is >25 kg/m2 and obese when >30 kg/m2. Noninvasive ventilation (NIV) by mask has become a popular method of treating respiratory distress and acute respiratory failure. Similar to what is being seen in mechanical ventilation with an increased need for positive end expiratory pressure (PEEP), patients with increased BMI may need additional inspiratory positive airway pressure (IPAP) and expiratory positive airway pressure (EPAP). This study aimed to answer the question: What is the role of BMI when setting EPAP and IPAP? It was hypothesized that BMI does not significantly impact starting level of EPAP and IPAP. Methods: This study utilized a retrospective review of the electronic medical record of patients who underwent treatment with NIV between 2/17/19 and 3/31/19. IRB approval was obtained, and patient identifiers were removed after data collection was completed. The respiratory therapists’ assessment of respiratory rate in breaths/min as well as vital signs for SpO2 and arterial blood gas values were assessed. The final IPAP and EPAP settings were recorded once the patient condition improved. Results: 136 patients were provided NIV treatment. 129 were placed on Bi-Level Positive Airway Pressure and 7 on continuous positive airway pressure. There were 27 patients with a BMI 25 kg/m2. All data was placed into IBM SPSS software v25 (IBM, Armonk, New York) and an unpaired t-test was used to compare final IPAP and EPAP settings of the two groups. There was a statistically significant difference in final EPAP settings (P = 0.02) between patients that have a recorded BMI of >25 kg/m2 and those that are 25 kg/m2 required a mean EPAP of 8.78 ± 1.96 cm H2O and an IPAP of 16.50 ± 3.46 cm H2O. Conclusions: This study shows that patients with an increased BMI (>25 kg/m2) may require increased EPAP when undergoing NIV treatment to improve clinical outcomes. This study also suggests that starting levels of EPAP should be considered based on a patient’s BMI. Disclosures: None

Background: Effective communication and collaboration between multidisciplinary teams are crucial for quality patient care. It is essential for registered nurses (RNs) and respiratory therapists (RTs) to be viewed as one bedside care team, seamlessly coordinating plans of care and medical management. Patient experience is impacted when respect among teams are demonstrated and perceived by the patient. A workgroup of staff RNs and RTs from a medical/pulmonary nursing floor (4SW) was assembled with RN/RT leadership serving as facilitators. The focus was to evaluate perception of communication among RNs and RTs, develop and implement a focused action plan to determine if perception of communication could be improved. Methods: The 4SW workgroup developed a survey of staff RNs and RTs from 4SW to understand current perceptions of communication and to identify areas of opportunity. Top focus areas for opportunity were phone interactions, RT understanding of patient condition when called and RN awareness of RT timeframe. The action plan included RN/RT call logs, RNs scripting for patient assessment and urgency level, and RTs providing expected time of arrival. Results: The post implementation surveys showed a 30% improvement in the perception of communication from RTs and no change of perceptions of RNs. Further analysis of the call logs shows accurate assessment of patient condition by RN 69% of time, appropriate urgency level assigned by RN 56% of the time, and arrival within 5 minutes of expected time frame given by RT 96% of the time. Conclusions: By creating an action plan by key stakeholders, this workgroup demonstrated improvement in perception of communication for RT staff. Additional work is needed to evaluate additional opportunities and barriers. Further research is needed to understand larger impacts, including patient satisfaction, employee satisfaction, and patient outcomes.