Mayo Clinic College of Medicine - Rochester

Mayo Clinic: Department of Anesthesiology and Perioperative Medicine

The Department of Anesthesiology and Perioperative Medicine-Research at Mayo Clinic conducts a wide range of research related to anesthetic techniques, perioperative medicine and pain management.

Investigators in the department have diverse research interests, ranging from very basic investigations of anesthetic effects on subcellular function to advanced clinical projects that have a direct impact on patient care, such as reducing the need for perioperative transfusions and improving the management of chronic pain.

With an extensive multidisciplinary faculty, the Department of Anesthesiology and Perioperative Medicine-Research is developing cutting-edge technologies in anesthesiology, including the use of gene therapy to treat cardiovascular disease in the perioperative period. The department engages in collaborative research with numerous Mayo Clinic physicians and scientists on a broad spectrum of conditions that benefit from improved anesthetic techniques and pain management or that require specialized care, including anesthesiology use in obstetrics and pediatrics.

The Department of Anesthesiology and Perioperative Medicine-Research seeks a better understanding of genomics, proteomics, lung biology, pharmacology and other basic science related to anesthesia to ultimately improve outcomes for patients in need of anesthesia and pain management.

 

CLINICAL INFORMATICS IN INTENSIVE CARE

Lipatov K, Daniels CE, Park JG, Elmer J, Hanson AC, Madsen BE, Clements CM, Gajic O, Pickering BW, Herasevich V. Implementation and evaluation of sepsis surveillance and decision support in medical ICU and emergency department. Am J Emerg Med. 2022 Jan;51:378-383. PMID: 34823194.

To improve the timely diagnosis and treatment of sepsis many institutions implemented automated sepsis alerts. We aimed to compare sepsis care compliance before and after a multi-year implementation of a sepsis surveillance coupled with decision support in a tertiary care center. Patients 18 years of age or older admitted to MICU and ED over 7 year period with severe sepsis or septic shock. The performance of the alert was modest with a sensitivity of 79.9%, specificity of 76.9%, positive predictive value (PPV) 27.9%, and negative predictive value (NPV) 97.2%. There were 3424 unique alerts and 1131 confirmed sepsis patients after the sniffer implementation. During the study period average care bundle compliance was higher; however after taking into account improvements in compliance leading up to the intervention, there was no association between intervention and improved care bundle compliance (Odds ratio: 1.16; 95% CI: 0.71 to 1.89; p-value 0.554). Similarly, the intervention was not associated with improvement in hospital mortality (Odds ratio: 1.55; 95% CI: 0.95 to 2.52; p-value: 0.078). A sepsis surveillance system incorporating decision support or completion feedback was not associated with improved sepsis care and patient outcomes.

 

 

Pinevich Y, Amos-Binks A, Burris CS, Rule G, Bogojevic M, Flint I, Pickering BW, Nemeth CP, Herasevich V. Validation of a Machine Learning Model for Early Shock Detection. Mil Med. 2022 Jan 4;187(1-2):82-88. PMID: 34056656.

The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity, and other estimates of diagnostic performance compared to the gold standard of electronic medical records (EMRs) review. A prospective cohort consisted of consecutive patients aged 18 years and older who were admitted from May 1 through September 30, 2020 to six Mayo Clinic intensive care units (ICUs) and five progressive care units. The area under the receiver operating characteristics curve for the 4TDS shock model was 0.86 (95% CI 0.85-0.87%). The 4TDS shock model demonstrated a sensitivity of 78.6% (95% CI 74.1-82.7%) and a specificity of 93.1% (95% CI 92.4-93.8%). The model showed a positive predictive value of 45.4% (95% CI 42.6-48.3%) and a negative predictive value of 98.4% (95% CI 98-98.6%). We successfully validated an ML model to detect circulatory shock in a prospective observational study.

 

 

Esmaeilzadeh S, Lane CM, Gerberi DJ, Wakeam E, Pickering BW, Herasevich V, Hyder JA. Improving In-Hospital Patient Rescue: What Are Studies on Early Warning Scores Missing? A Scoping Review. Crit Care Explor. 2022 Feb 21;4(2):e0644. PMID: 35224506

 

Administrative and clinical efforts to improve hospital mortality and intensive care utilization commonly focus on patient rescue, where deteriorating patients are systematically identified and intervened upon. With widespread adoption of electronic medical records, early warning score (EWS) systems, which assign points to clinical data elements, are increasingly promoted as a tool for timely patient rescue by referencing their prediction of patient deterioration. We describe the extent to which EWS intervention studies describe the hospital environment of the intervention-details that would be critical for hospital leaders attempting to determine the real-world utility of EWSs in their own hospitals. We searched CINAHL, PubMed, and Scopus databases for English language EWS implementation research published between 2009 and 2021 in adult medical-surgical inpatients. The 21 studies (18 before-and-after, three randomized trials) detailed 1,107,883 patients across 54 hospitals. While 16 qualitatively described resources for education/technologic implementation, none estimated costs. Despite hundreds of EWS-related publications, most do not report details of hospital context that would inform decisions about real-world EWS adoption.

 

 

Park J, Zhong X, Dong Y, Barwise A, Pickering BW. Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach. BMC Anesthesiol. 2022 Jan 4;22(1):10. doi: 10.1186/s12871-021-01548-7. PMID: 34983402; PMCID: PMC8724599.

 

ICU operational conditions may contribute to cognitive overload and negatively impact on clinical decision making. We aimed to develop a quantitative model to investigate the association between the operational conditions and the quantity of medication orders as a measurable indicator of the multidisciplinary care team’s cognitive capacity. Methods: The temporal data of patients at one medical ICU (MICU) of Mayo Clinic in Rochester, MN between February 2016 to March 2018 was used. This dataset includes a total of 4822 unique patients admitted to the MICU and a total of 6240 MICU admissions. Guided by the Systems Engineering Initiative for Patient Safety model, quantifiable measures attainable from electronic medical records were identified and a conceptual framework of distributed cognition in ICU was developed. Univariate piecewise Poisson regression models were built to investigate the relationship between system-level workload indicators, including patient census and patient characteristics (severity of illness, new admission, and mortality risk) and the quantity of medication orders, as the output of the care team’s decision making. Results: Comparing the coefficients of different line segments obtained from the regression models using a generalized F-test, we identified that, when the ICU was more than 50% occupied (patient census > 18), the number of medication orders per patient per hour was significantly reduced (average = 0.74; standard deviation (SD) = 0.56 vs. average = 0.65; SD = 0.48; p < 0.001). The reduction was more pronounced (average = 0.81; SD = 0.59 vs. average = 0.63; SD = 0.47; p < 0.001), and the breakpoint shifted to a lower patient census (16 patients) when at a higher presence of severely-ill patients requiring invasive mechanical ventilation during their stay, which might be encountered in an ICU treating patients with COVID-19. Conclusions: Our model suggests that ICU operational factors, such as admission rates and patient severity of illness may impact the critical care team’s cognitive function and result in changes in the production of medication orders. The results of this analysis heighten the importance of increasing situational awareness of the care team to detect and react to changing circumstances in the ICU that may contribute to cognitive overload.

 

 

Huang C, Barwise A, Soleimani J, Dong Y, Svetlana H, Khan SA, Gavin A, Helgeson SA, Moreno-Franco P, Pinevich Y, Kashyap R, Herasevich V, Gajic O, Pickering BW. Bedside Clinicians' Perceptions on the Contributing Role of Diagnostic Errors in Acutely Ill Patient Presentation: A Survey of Academic and Community Practice. J Patient Saf. 2022 Mar 1;18(2):e454-e462. doi: 10.1097/PTS.0000000000000840. PMID: 35188935.

 

This study aimed to explore clinicians' perceptions of the occurrence of and factors associated with diagnostic errors in patients evaluated during a rapid response team (RRT) activation or unplanned admission to the intensive care unit (ICU). Methods A multicenter prospective survey study was conducted among multiprofessional clinicians involved in the care of patients with RRT activations and/or unplanned ICU admissions (UIAs) at 2 academic hospitals and 1 community-based hospital between April 2019 and March 2020. A study investigator screened eligible patients every day. Within 24 hours of the event, a research coordinator administered the survey to clinicians, who were asked the following: whether diagnostic errors contributed to the reason for RRT/UIA, whether any new diagnosis was made after RRT/UIA, if there were any failures to communicate the diagnosis, and if involvement of specialists earlier would have benefited that patient. Patient clinical data were extracted from the electronic health record. Results A total of 1815 patients experienced RRT activations, and 1024 patients experienced UIA. Clinicians reported that 18.2% (95/522) of patients experienced diagnostic errors, 8.0% (42/522) experienced a failure of communication, and 16.7% (87/522) may have benefitted from earlier involvement of specialists. Compared with academic settings, clinicians in the community hospital were less likely to report diagnostic errors (7.0% versus 22.8%, P = 0.002). Conclusions Clinicians report a high rate of diagnostic errors in patients they evaluate during RRT or UIAs.

 

 

Xiang Zhong, Farnaz Babaie Sarijaloo, Aditya Prakash, Jaeyoung Park, Chanyan Huang, Amelia Barwise, Vitaly Herasevich, Ognjen Gajic, Brian Pickering & Yue Dong (2022) A multidisciplinary approach to the development of digital twin models of critical care delivery in intensive care units, International Journal of Production Research, DOI: 10.1080/00207543.2021.2022235

 

To investigate critical care delivery in intensive care units (ICUs), we propose a qualitative and quantitative coupling approach to developing an ICU digital twin model. The Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model was adapted to conceptualise the current ICU system. A hybrid simulation model was developed to characterise major care delivery processes as discrete-time events, feature patients, clinicians, and other artifacts as autonomous agents, and integrate them in the same simulation environment to capture their interactions under a variety of ICU production conditions. Electronic health record (EHR) data from a medical ICU of Mayo Clinic Rochester, Minnesota, were used to calibrate model parameters. Upon iterative refinement and validation, the model has the potential to be integrated with the hospital information system to simulate real-life events as a full-fledged digital twin of the system. It can be used as an in-silico testbed to investigate the real-time allocation of ICU resources such as medical equipment, flexible staffing, workflow change, and support decisions of patient admission, discharge, and transfer, for healthcare delivery innovation. The interdisciplinary nature of this framework demonstrates and promotes the partnership between healthcare and engineering communities to building a better delivery system.

 

 

BIOADHESIVE TREATMENT

Jingjing Wu, Hyunwoo Yuk, Tiffany L Sarrafian, Chuan Fei Guo, Leigh G Griffiths, Christoph S Nabzdyk, Xuanhe Zhao. An off-the-shelf bioadhesive patch for sutureless repair of gastrointestinal defects. Sci Transl Med. 2022 Feb 2;14(630):eabh2857. doi: 10.1126/scitranslmed.abh2857. Epub 2022 Feb 2. PMID: 35108064.

 

Surgical sealing and repair of injured and resected gastrointestinal (GI) organs are critical requirements for successful treatment and tissue healing. Despite being the standard of care, hand-sewn closure of GI defects using sutures faces limitations and challenges. In this work, we introduce an off-the-shelf bioadhesive GI patch capable of atraumatic, rapid, robust, and sutureless repair of GI defects. The GI patch integrates a nonadhesive top layer and a dry, bioadhesive bottom layer, resulting in a thin, flexible, transparent, and ready-to-use patch with tissue-matching mechanical properties. The rapid, robust, and sutureless sealing capability of the GI patch is systematically characterized using ex vivo porcine GI organ models. In vitro and in vivo rat models are used to evaluate the biocompatibility and degradability of the GI patch in comparison to commercially available tissue adhesives (Coseal and Histoacryl). To validate the GI patch's efficacy, we demonstrate successful sutureless in vivo sealing and healing of GI defects in rat colon, stomach, and small intestine as well as in porcine colon injury models. The proposed GI patch provides a promising alternative to suture for repair of GI defects and offers potential clinical opportunities for the repair of other organs.