BACKGROUND:Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients.RESEARCH QUESTION:Can a predictive model be developed to accurately identify patients at high risk of PRF?STUDY DESIGN AND METHODS:In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores.RESULTS:Two hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203).INTERPRETATION:This single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.

Background Few interventions are known to reduce the incidence of respiratory failure that occurs following elective surgery (postoperative respiratory failure; PRF). We previously reported risk factors associated with PRF that occurs within the first 5 days after elective surgery (early PRF; E-PRF); however, PRF that occurs six or more days after elective surgery (late PRF; L-PRF) likely represents a different entity. We hypothesized that L-PRF would be associated with worse outcomes and different risk factors than E-PRF. Methods This was a retrospective matched case-control study of 59,073 consecutive adult patients admitted for elective non-cardiac and non-pulmonary surgical procedures at one of five University of California academic medical centers between October 2012 and September 2015. We identified patients with L-PRF, confirmed by surgeon and intensivist subject matter expert review, and matched them 1:1 to patients who did not develop PRF (No-PRF) based on hospital, age, and surgical procedure. We then analyzed risk factors and outcomes associated with L-PRF compared to E-PRF and No-PRF. Results Among 95 patients with L-PRF, 50.5% were female, 71.6% white, 27.4% Hispanic, and 53.7% Medicare recipients; the median age was 63 years (IQR 56, 70). Compared to 95 matched patients with No-PRF and 319 patients who developed E-PRF, L-PRF was associated with higher morbidity and mortality, longer hospital and intensive care unit length of stay, and increased costs. Compared to No-PRF, factors associated with L-PRF included: preexisiting neurologic disease (OR 4.36, 95% CI 1.81–10.46), anesthesia duration per hour (OR 1.22, 95% CI 1.04–1.44), and maximum intraoperative peak inspiratory pressure per cm H 2 0 (OR 1.14, 95% CI 1.06–1.22). Conclusions We identified that pre-existing neurologic disease, longer duration of anesthesia, and greater maximum intraoperative peak inspiratory pressures were associated with respiratory failure that developed six or more days after elective surgery in adult patients (L-PRF). Interventions targeting these factors may be worthy of future evaluation.

In this narrative review, anesthesiologists at 2 large hospital systems in New York City and San Francisco compare early coronavirus disease 2019 (COVID-19) public health outcomes while considering the role played by social vulnerability and relevant approaches in their 2 cities. An iterative search process allowed for a broad review of medical and public policy research, as well as newspaper reports, expert opinion, and multimedia sources, with the goal of exploring the importance of crowding, the labor force, and social identity in pandemic experiences. Related struggles, pitfalls, and successful interventions in both locales are summarized. Although technology in the form of vaccination will likely play an outsize role in the next phase of the pandemic, our review concludes that we must carefully consider how social vulnerabilities have and will continue to inform equitable and effective access to life-saving resources.

BACKGROUND:Guidelines currently recommend targeting light sedation with dexmedetomidine or propofol for adults receiving mechanical ventilation. Differences exist between these sedatives in arousability, immunity, and inflammation. Whether they affect outcomes differentially in mechanically ventilated adults with sepsis undergoing light sedation is unknown.METHODS:In a multicenter, double-blind trial, we randomly assigned mechanically ventilated adults with sepsis to receive dexmedetomidine (0.2 to 1.5 μg per kilogram of body weight per hour) or propofol (5 to 50 μg per kilogram per minute), with doses adjusted by bedside nurses to achieve target sedation goals set by clinicians according to the Richmond Agitation-Sedation Scale (RASS, on which scores range from -5 [unresponsive] to +4 [combative]). The primary end point was days alive without delirium or coma during the 14-day intervention period. Secondary end points were ventilator-free days at 28 days, death at 90 days, and age-adjusted total score on the Telephone Interview for Cognitive Status questionnaire (TICS-T; scores range from 0 to 100, with a mean of 50±10 and lower scores indicating worse cognition) at 6 months.RESULTS:Of 432 patients who underwent randomization, 422 were assigned to receive a trial drug and were included in the analyses - 214 patients received dexmedetomidine at a median dose of 0.27 μg per kilogram per hour, and 208 received propofol at a median dose of 10.21 μg per kilogram per minute. The median duration of receipt of the trial drugs was 3.0 days (interquartile range, 2.0 to 6.0), and the median RASS score was -2.0 (interquartile range, -3.0 to -1.0). We found no difference between dexmedetomidine and propofol in the number of days alive without delirium or coma (adjusted median, 10.7 vs. 10.8 days; odds ratio, 0.96; 95% confidence interval [CI], 0.74 to 1.26), ventilator-free days (adjusted median, 23.7 vs. 24.0 days; odds ratio, 0.98; 95% CI, 0.63 to 1.51), death at 90 days (38% vs. 39%; hazard ratio, 1.06; 95% CI, 0.74 to 1.52), or TICS-T score at 6 months (adjusted median score, 40.9 vs. 41.4; odds ratio, 0.94; 95% CI, 0.66 to 1.33). Safety end points were similar in the two groups.CONCLUSIONS:Among mechanically ventilated adults with sepsis who were being treated with recommended light-sedation approaches, outcomes in patients who received dexmedetomidine did not differ from outcomes in those who received propofol. (Funded by the National Institutes of Health; ClinicalTrials.gov number, NCT01739933.).

Article| October 2021 What Is an ‘OR’ in 2030: Anesthesia Beyond the Operating Room Michael A. Gropper, MD, PhD Michael A. Gropper, MD, PhD Search for other works by this author on: This Site PubMed Google Scholar ASA Monitor October 2021, Vol. 85, 21–23. https://doi.org/10.1097/01.ASM.0000795160.13724.53 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Cite Icon Cite Get Permissions Search Site Citation Michael A. Gropper; What Is an ‘OR’ in 2030: Anesthesia Beyond the Operating Room. ASA Monitor 2021; 85:21–23 doi: https://doi.org/10.1097/01.ASM.0000795160.13724.53 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll PublicationsASA Monitor Search Advanced Search Topics: inpatients, operating room, perioperative care, surgical procedures, operative, patient-centered care, telemonitoring, deep learning, outpatients While it is impossible to predict the future of anesthesiology, we can learn much from current trends in perioperative care. Advances in technology are only part of the future. Perhaps more important than technology has been the recognition that all of our interventions should be centered on improving patient outcomes. As the health care system has gotten more complex and costly, patients are exercising their right to focus care on their goals and expectations. This is particularly true for patients with complex medical conditions and those requiring major surgical procedures. In addition, as the American populace ages, there is increasing demand for surgical and anesthesia services, particularly for age-related conditions such as arthritis, cardiac disease, and cancer. One of the most important and patient-centered innovations in health care has been the development of the patient-centered medical home (PCMH). There are five key functions and attributes of the PCMH as defined... You do not currently have access to this content.

BACKGROUND:Postoperative respiratory failure is the most common serious postoperative pulmonary complication, yet little is known about factors that can reduce its incidence. We sought to elucidate modifiable factors associated with respiratory failure that developed within the first 5 d after an elective operation.MATERIALS AND METHODS:Matched case-control study of adults who had an operation at five academic medical centers between October 1, 2012 and September 30, 2015. Cases were identified using administrative data and confirmed via chart review by critical care clinicians. Controls were matched 1:1 to cases based on hospital, age, and surgical procedure.RESULTS:Our total sample (n = 638) was 56.4% female, 71.3% white, and had a median age of 62 y (interquartile range 51, 70). Factors associated with early postoperative respiratory failure included male gender (odds ratio [OR] 1.72, 95% confidence interval [CI] 1.12-2.63), American Society of Anesthesiologists class III or greater (OR 2.85, 95% CI 1.74-4.66), greater number of preexisting comorbidities (OR 1.14, 95% CI 1.004-1.30), increased operative duration (OR 1.14, 95% CI 1.06-1.22), increased intraoperative positive end-expiratory pressure (OR 1.23, 95% CI 1.13-1.35) and tidal volume (OR 1.13, 95% CI 1.004-1.27), and greater net fluid balance at 24 h (OR 1.17, 95% CI 1.07-1.28).CONCLUSIONS:We found greater intraoperative ventilator volume and pressure and 24-h fluid balance to be potentially modifiable factors associated with developing early postoperative respiratory failure. Further studies are warranted to independently verify these risk factors, explore their role in development of early postoperative respiratory failure, and potentially evaluate targeted interventions.

OBJECTIVE During the COVID-19 pandemic, quaternary-care facilities continue to provide care for patients in need of urgent and emergent invasive procedures. Perioperative protocols are needed to streamline care for these patients notwithstanding capacity and resource constraints. METHODS A multidisciplinary panel was assembled at the University of California, San Francisco, with 26 leaders across 10 academic departments, including 7 department chairpersons, the chief medical officer, the chief operating officer, infection control officers, nursing leaders, and resident house staff champions. An epidemiologist, an ethicist, and a statistician were also consulted. A modified two-round, blinded Delphi method based on 18 agree/disagree statements was used to build consensus. Significant disagreement for each statement was tested using a one-sided exact binomial test against an expected outcome of 95% consensus using a significance threshold of p < 0.05. Final triage protocols were developed with unblinded group-level discussion. RESULTS Overall, 15 of 18 statements achieved consensus in the first round of the Delphi method; the 3 statements with significant disagreement (p < 0.01) were modified and iteratively resubmitted to the expert panel to achieve consensus. Consensus-based protocols were developed using unblinded multidisciplinary panel discussions. The final algorithms 1) quantified outbreak level, 2) triaged patients based on acuity, 3) provided a checklist for urgent/emergent invasive procedures, and 4) created a novel scoring system for the allocation of personal protective equipment. In particular, the authors modified the American College of Surgeons three-tiered triage system to incorporate more urgent cases, as are often encountered in neurosurgery and spine surgery. CONCLUSIONS Urgent and emergent invasive procedures need to be performed during the COVID-19 pandemic. The consensus-based protocols in this study may assist healthcare providers to optimize perioperative care during the pandemic.

Background: Administrative data can be used to identify cases of postoperative respiratory failure (PRF). We aimed to determine if recent changes to the Agency for Healthcare Research and Quality Patient Safety Indicator 11 (PSI 11) and adoption of clinical documentation improvement programs have improved the validity of PSI 11. We also analyzed reasons why PSI 11 was falsely triggered. Study design: Cross-sectional study of all eligible discharges using health record data from five academic medical centers between October 1, 2012 and September 30, 2015. Results: Of 437 flagged records, 434 (99.3%) were accurately coded and 414 (94.7%) represented true clinical PRF. None of the false positive records involved respiratory failure present on admission. Most (78.3%) false positive records required airway protection but did not have respiratory failure. Conclusion: The validity of PSI 11 has improved with recent changes to the code criterion and adoption of clinical documentation improvement programs. (C) 2019 Elsevier Inc. All rights reserved.

As part of an interdisciplinary acute care patient portal task force with members from 10 academic medical centers and professional organizations, we held a national workshop with 71 attendees representing over 30 health systems, professional organizations, and technology companies. Our consensus approach identified 7 key sociotechnical and evaluation research focus areas related to the consumption and capture of information from patients, care partners (eg, family, friends), and clinicians through portals in the acute and post-acute care settings. The 7 research areas were: (1) standards, (2) privacy and security, (3) user-centered design, (4) implementation, (5) data and content, (6) clinical decision support, and (7) measurement. Patient portals are not yet in routine use in the acute and post-acute setting, and research focused on the identified domains should increase the likelihood that they will deliver benefit, especially as there are differences between needs in acute and post-acute care compared to the ambulatory setting.

Objectives This study aimed to use a systems engineering approach to improve performance and stakeholder engagement in the intensive care unit to reduce several different patient harms. Methods We developed a conceptual framework or concept of operations (ConOps) to analyze different types of harm that included 4 steps as follows: risk assessment, appropriate therapies, monitoring and feedback, as well as patient and family communications. This framework used a transdisciplinary approach to inventory the tasks and work flows required to eliminate 7 common types of harm experienced by patients in the intensive care unit. The inventory gathered both implicit and explicit information about how the system works or should work and converted the information into a detailed specification that clinicians could understand and use. Prototype ConOps to Eliminate Harm Using the ConOps document, we created highly detailed work flow models to reduce harm and offer an example of its application to deep venous thrombosis. In the deep venous thrombosis model, we identified tasks that were synergistic across different types of harm. We will use a system of systems approach to integrate the variety of subsystems and coordinate processes across multiple types of harm to reduce the duplication of tasks. Through this process, we expect to improve efficiency and demonstrate synergistic interactions that ultimately can be applied across the spectrum of potential patient harms and patient locations. Conclusions Engineering health care to be highly reliable will first require an understanding of the processes and work flows that comprise patient care. The ConOps strategy provided a framework for building complex systems to reduce patient harm.

Objectives: We describe the importance of interprofessional care in modern critical care medicine. This review highlights the essential roles played by specific members of the interprofessional care team, including patients and family members, and discusses quality improvement initiatives that require interprofessional collaboration for success. Data Sources: Studies were identified through MEDLINE search using a variety of search phrases related to interprofessional care, critical care provider types, and quality improvement initiatives. Additional articles were identified through a review of the reference lists of identified articles. Study Selection: Original articles, review articles, and systematic reviews were considered. Data Extraction: Manuscripts were selected for inclusion based on expert opinion of well-designed or key studies and review articles. Data Synthesis: “Interprofessional care” refers to care provided by a team of healthcare professionals with overlapping expertise and an appreciation for the unique contribution of other team members as partners in achieving a common goal. A robust body of data supports improvement in patient-level outcomes when care is provided by an interprofessional team. Critical care nurses, advanced practice providers, pharmacists, respiratory care practitioners, rehabilitation specialists, dieticians, social workers, case managers, spiritual care providers, intensivists, and nonintensivist physicians each provide unique expertise and perspectives to patient care, and therefore play an important role in a team that must address the diverse needs of patients and families in the ICU. Engaging patients and families as partners in their healthcare is also critical. Many important ICU quality improvement initiatives require an interprofessional approach, including Awakening and Breathing Coordination, Delirium, Early Exercise/Mobility, and Family Empowerment bundle implementation, interprofessional rounding practices, unit-based quality improvement initiatives, Patient and Family Advisory Councils, end-of-life care, coordinated sedation awakening and spontaneous breathing trials, intrahospital transport, and transitions of care. Conclusions: A robust body of evidence supports an interprofessional approach as a key component in the provision of high-quality critical care to patients of increasing complexity and with increasingly diverse needs.

Project Emerge took a systems engineering approach to reduce avoidable harm in the intensive care unit. We developed a socio-technology solution to aggregate and display information relevant to preventable patient harm. We compared providers' efficiency and ability to assess and assimilate data associated with patient-safety practice compliance using the existing electronic health record to Emerge, and evaluated for speed, accuracy, and the number of mouse clicks required. When compared to the standard electronic health record, clinicians were faster (529 ± 210 s vs 1132 ± 344 s), required fewer mouse clicks (42.3 ± 15.3 vs 101.3 ± 33.9), and were more accurate (24.8 ± 2.7 of 28 correct vs 21.2 ± 2.9 of 28 correct) when using Emerge. All results were statistically significant at a p-value < 0.05 using Wilcoxon signed-rank test (n = 18). Emerge has the potential to make clinicians more productive and patients safer by reducing the time and errors when obtaining information to reduce preventable harm.