Preoperative Management of the Adult Oncology Patient

Anesthesiologists are experiencing first-hand the aging population, given older patients more frequently presenting for surgery, often with geriatric syndromes influencing their anesthetic management. The overall incidence and health burden of cancer morbidity and mortality are also rapidly increasing worldwide. This growth in the cancer population, along with the associated risk factors and comorbidities often accompanying a cancer diagnosis, underscores the need for anesthesiologists to become well versed in the preoperative evaluation and management of the adult patient with cancer. This article will focus on the unique challenges and opportunities for the anesthesiologist caring for the adult oncology patient presenting for surgery.

Social Determinants of Health and Preoperative Care

Preoperative care exists as part of perioperative continuum during which anesthesiologists and surgeons optimize patients for surgery. These multispecialty efforts are important, particularly for patients with complex medical histories and those requiring major surgery. Preoperative care improves planning and determines the clinical pathway and discharge disposition. The role of nonmedical social factors in the preoperative planning is not well described in anesthesiology. Research to improve outcomes based on social factors is not well described for anesthesiologists but could be instrumental in decreasing disparities and advancing health equity in surgical patients.

Assuring the Groundwork for Success: Mentorship, Sponsorship, and Allyship for Practicing Anesthesiologists

The challenges facing the health care industry in the post-coronavirus disease 2019 (COVID-19) pandemic world are numerous, jeopardizing wellness, and performance. Maintaining engagement and fulfillment of anesthesiologists in their work is now a critical issue in various practice settings: academic, private practice, and corporate medicine. In this article, we offer insights on how mentorship, sponsorship, and allyship are important in the advancement of the anesthesiology workforce including women and underrepresented minorities inclusive of race, gender, and disability. Mentorship, sponsorship, and allyship require a framework that intentionally addresses the programmatic structures needed to optimize the environment for increasing women, underrepresented minorities, and other diverse groups. These 3 distinct yet interrelated concepts are defined with a discussion on the value of implementation. In addition, the concept of "belonging" and its importance in enhancing the culture in anesthesiology is explored. We believe that part of the solution to wellness, recruitment and retention and improved job satisfaction of clinicians is having an environment where mentorship, sponsorship, and allyship are foundational.

Sociodemographic Disparities in Postoperative Nausea and Vomiting

BACKGROUND

Postoperative nausea and vomiting (PONV) prophylaxis is consistently considered a key indicator of anesthesia care quality. PONV may disproportionately impact disadvantaged patients. The primary objectives of this study were to examine the associations between sociodemographic factors and the incidence of PONV and clinician adherence to a PONV prophylaxis protocol.

METHODS

We conducted a retrospective analysis of all patients eligible for an institution-specific PONV prophylaxis protocol (2015-2017). Sociodemographic and PONV risk data were collected. Primary outcomes were PONV incidence and clinician adherence to PONV prophylaxis protocol. We used descriptive statistics to compare sociodemographics, procedural characteristics, and protocol adherence for patients with and without PONV. Multivariable logistic regression analysis followed by Tukey-Kramer correction for multiple comparisons was used to test for associations between patient sociodemographics, procedural characteristics, PONV risk, and (1) PONV incidence and (2) adherence to PONV prophylaxis protocol.

RESULTS

Within the 8384 patient sample, Black patients had a 17% lower risk of PONV than White patients (adjusted odds ratio [aOR], 0.83; 95% confidence interval [CI], 0.73-0.95; P = .006). When there was adherence to the PONV prophylaxis protocol, Black patients were less likely to experience PONV compared to White patients (aOR, 0.81; 95% CI, 0.70-0.93; P = .003). When there was adherence to the protocol, patients with Medicaid were less likely to experience PONV compared to privately insured patients (aOR, 0.72; 95% CI, 0.64-1.04; P = .017). When the protocol was followed for high-risk patients, Hispanic patients were more likely to experience PONV than White patients (aOR, 2.96; 95% CI, 1.18-7.42; adjusted P = .022). Compared to White patients, protocol adherence was lower for Black patients with moderate (aOR, 0.76; 95% CI, 0.64-0.91; P = .003) and high risk (aOR, 0.57; 95% CI, 0.42-0.78; P = .0004).

CONCLUSIONS

Racial and sociodemographic disparities exist in the incidence of PONV and clinician adherence to a PONV prophylaxis protocol. Awareness of such disparities in PONV prophylaxis could improve the quality of perioperative care.

Recognizing and Maximizing the Nexus of Perioperative Medicine and Narrative Medicine

Perioperative medicine remains an evolving, interdisciplinary subspecialty, which encompasses the unique perspectives and incorporates the respective vital expertise of numerous stakeholders. This integrated model of perioperative medicine and care has a wide-ranging set of clinical, strategic, and operational goals. Among these various programmatic goals, a subset of 4, specific, interdependent goals include (1) enhancing patient-centered care, (2) embracing shared decision-making, (3) optimizing health literacy, and (4) avoiding futile surgery. Achieving and sustaining this subset of 4 goals requires continued innovative approaches to perioperative care. The burgeoning field of narrative medicine represents 1 such innovative approach to perioperative care. Narrative medicine is considered the most prominent recent development in the medical humanities. Its central tenet is that attention to narrative-in the form of the patient's story, the clinician's story, or a story constructed together by the patient and clinician-is essential for optimal patient care. If we can view the health care experience through the patient's eyes, we will become more responsive to patients' needs and, thereby, better clinicians. There is a potential clinical nexus between the perioperative medicine practice and narrative medicine skills, which, if capitalized, can maximize perioperative patient care. There are a number of untapped educational and research opportunities in this fruitful nexus between perioperative medicine and narrative medicine.

Utility of the LET-IN-OUT Clinical Decision Support Tool for Medical Risk Stratification Prior to Outpatient Total Hip or Knee Arthroplasty

BACKGROUND

Musculoskeletal care teams can benefit from simple, standardized, and reliable preoperative tools for assessing discharge disposition after total joint arthroplasty. Our objective was to compare the predictive strength of the Ascension Seton Lower Extremity Inpatient-Outpatient (LET-IN-OUT) tool versus the American Society of Anesthesiologists Physical Status (ASA-PS) score for predicting early postoperative discharge.

METHODS

We retrospectively extracted sociodemographic, surgical admission, postoperative day (POD) of discharge, 90-day readmissions, and predictions of the LET-IN-OUT and ASA-PS tools from the electronic records of 563 consecutive hip or knee arthroplasty patients (mean age 65 [SD 9.6], 54% women). Included patients who underwent a total hip arthroplasty (THA) or total knee arthroplasty (TKA) at a single health system between June 2020 and March 2021. We performed descriptive statistics and analyzed predictive values of each tool, defining "early discharge" primarily as discharge before the second postoperative day (POD 2), and secondarily as before 24 hours, and on the same calendar day (POD 0) as surgery.

RESULTS

The LET-IN-OUT tool demonstrated superior predictive power among hip and knee arthroplasty patients compared to the ASA-PS tool for discharge prior to POD 2 (positive predictive value [PPV] 89 versus 83%, positive likelihood ratio [+LR] 2.0 versus 1.2), discharge before 24 hours (PPV 86 versus 70%, +LR 2.9 versus 1.2), and discharge on POD 0 (PPV 34% versus 30%, +LR 1.2 versus 1.1).

CONCLUSIONS

The Ascension Seton Lower Extremity Inpatient-Outpatient tool predicted patients suitable for early discharge following THA or TKA and did so more effectively than the ASA-PS score.

Achieving Greater Health Equity: An Opportunity for Anesthesiology

Anesthesiology and anesthesiologists have a tremendous opportunity and responsibility to eliminate health disparities and to achieve health equity. We thus examine health disparity and health equity through the lens of anesthesiology and the perspective of anesthesiologists. In this paper, we define health disparity and health care disparities and provide tangible, representative examples of the latter in the practice of anesthesiology. We define health equity, primarily as the desired antithesis of health disparity. Finally, we propose a framework for anesthesiologists, working toward mitigating health disparity and health care disparities, advancing health equity, and documenting improvements in health care access and health outcomes. This multilevel and interdependent framework includes the perspectives of the patient, clinician, group or department, health care system, and professional societies, including medical journals. We specifically focus on the interrelated roles of social identity and social determinants of health in health outcomes. We explore the foundational role that clinical informatics and valid data collection on race and ethnicity have in achieving health equity. Our ability to ensure patient safety by considering these additional patient-specific factors that affect clinical outcomes throughout the perioperative period could substantially reduce health disparities. Finally, we explore the role of medical journals and their editorial boards in ameliorating health disparities and advancing health equity.

Statistics From A (Agreement) to Z (z Score): A Guide to Interpreting Common Measures of Association, Agreement, Diagnostic Accuracy, Effect Size, Heterogeneity, and Reliability in Medical Research

Researchers reporting results of statistical analyses, as well as readers of manuscripts reporting original research, often seek guidance on how numeric results can be practically and meaningfully interpreted. With this article, we aim to provide benchmarks for cutoff or cut-point values and to suggest plain-language interpretations for a number of commonly used statistical measures of association, agreement, diagnostic accuracy, effect size, heterogeneity, and reliability in medical research. Specifically, we discuss correlation coefficients, Cronbach's alpha, I2, intraclass correlation (ICC), Cohen's and Fleiss' kappa statistics, the area under the receiver operating characteristic curve (AUROC, concordance statistic), standardized mean differences (Cohen's d, Hedge's g, Glass' delta), and z scores. We base these cutoff values on what has been previously proposed by experts in the field in peer-reviewed literature and textbooks, as well as online statistical resources. We integrate, adapt, and/or expand previous suggestions in attempts to (a) achieve a compromise between divergent recommendations, and (b) propose cutoffs that we perceive sensible for the field of anesthesia and related specialties. While our suggestions provide guidance on how the results of statistical tests are typically interpreted, this does not mean that the results can universally be interpreted as suggested here. We discuss the well-known inherent limitations of using cutoff values to categorize continuous measures. We further emphasize that cutoff values may depend on the specific clinical or scientific context. Rule-of-the thumb approaches to the interpretation of statistical measures should therefore be used judiciously.

Improving Adherence to Intraoperative Lung-Protective Ventilation Strategies Using Near Real-Time Feedback and Individualized Electronic Reporting

BACKGROUND

Postoperative pulmonary complications can have a significant impact on the morbidity and mortality of patients undergoing major surgeries. Intraoperative lung protective strategies using low tidal volume (TV) ventilation and positive end-expiratory pressure (PEEP) have been demonstrated to reduce the incidence of pulmonary injury and infection while improving oxygenation and respiratory mechanics. The purpose of this study was to develop decision support systems designed to optimize behavior of the attending anesthesiologist with regards to adherence with established intraoperative lung-protective ventilation (LPV) strategies.

METHODS

Over a 4-year period, data were obtained from 49,386 procedures and 109 attendings. Cases were restricted to patients aged 18 years or older requiring general anesthesia that lasted at least 60 minutes. We defined protective lung ventilation as a TV of 6-8 mL/kg ideal body weight and a PEEP of ≥4 cm H2O. There was a baseline period followed by 4 behavioral interventions: education, near real-time feedback, individualized post hoc feedback, and enhanced multidimensional decision support. Segmented logistic regression using generalized estimating equations was performed in order to assess temporal trends and effects of interventions on adherence to LPV strategies.

RESULTS

Consistent with improvement in adherence with LPV strategies during the baseline period, the predicted probability of adherence with LPV at the end of baseline was 0.452 (95% confidence interval [CI], 0.422-0.483). The improvements observed for each phase were relative to the preceding phase. Education alone was associated with an 8.7% improvement (P < .01) in adherence to lung-protective protocols and was associated with a 16% increase in odds of adherence (odds ratio [OR] = 1.16; 95% CI, 1.01-1.33; P = .04). Near real-time, on-screen feedback was associated with an estimated 15.5% improvement in adherence (P < .01) with a 69% increase in odds of adherence (OR = 1.69; 95% CI, 1.46-1.96; P < .01) over education alone. The addition of an individualized dashboard with personal adherence and peer comparison was associated with a significant improvement over near real-time feedback (P < .01). Near real-time feedback and dashboard feedback systems were enhanced based on feedback from the in-room attendings, and this combination was associated with an 18.1% (P < .01) increase in adherence with a 2-fold increase in the odds of adherence (OR = 2.23; 95% CI, 1.85-2.69; P < .0001) between the end of the previous on-screen feedback phase and the start of the individualized post hoc dashboard reporting phase. The adherence with lung-protective strategies using the multidimensional approach has been sustained for over 24 months. The difference between the end of the previous phase and the start of this last enhanced multidimensional decision support phase was not significant (OR = 1.08; 95% CI, 0.86-1.34; P = .48).

CONCLUSIONS

Consistent with the literature, near real-time and post hoc reporting are associated with positive and sustained behavioral changes aimed at adopting evidence-based clinical strategies. Many decision support systems have demonstrated impact to behavior, but the effect is often transient. The implementation of near real-time feedback and individualized post hoc decision support tools has resulted in clinically relevant improvements in adherence with LPV strategies that have been sustained for over 24 months, a common limitation of decision support solutions.

A Simple Tool for Recommending Postoperative Status After Lower Extremity Total Joint Replacement

There is an increasing impetus to perform primary total hip arthroplasty and total knee arthroplasty on an outpatient basis and in the outpatient setting. However, with recent federal regulatory changes, orthopedic surgeons must now evaluate patients on a case-by-case basis to determine whether an inpatient admission will be medically necessary and appropriate. We thus created our prototype Lower Extremity Inpatient-Outpatient (LET-IN-OUT) total joint replacement tool as a simple, consistent way for other clinicians to identify specific major preoperative patient comorbidities and thus to recommend independently and objectively to the orthopedic surgeon postoperative inpatient or outpatient status for a given patient.

Enhanced Selection of Candidates for Same-Day and Outpatient Total Knee Arthroplasty

BACKGROUND

Medicare removed total knee arthroplasty (TKA) from its inpatient-only list and private insurers created ambulatory surgical codes; these changes bring about logistical challenges for TKA episode planning. We identified preoperatively determined factors associated with hospital length of stay for (1) same-day discharge (SDD) and (2) inpatient TKA defined by Medicare's 2-midnight rule benchmark.

METHODS

We retrospectively reviewed 325 consecutive unilateral primary TKAs performed on patients completing the Perioperative Surgical Home preoperative optimization pathway within a single hospital system. Stepwise logistic regression modeling was performed to identify preoperatively determined factors associated with (1) SDD and (2) inpatient TKA. We compared these models' ability to discern the length of stay category to the Risk Assessment and Prediction Tool (RAPT) score alone.

RESULTS

The cohort included 32 (10%) SDD, 189 (58%) next-day discharges, and 104 (32%) inpatients. Lower body mass index (BMI; odds ratio [OR], 0.92; 95% CI, 0.85-0.1.0; P = .04) and fewer self-reported allergies (OR, 0.66; 95% CI, 0.46-0.95; P = .03) were associated with SDD. The SDD model outperformed the RAPT alone (C-statistic, 0.73 vs 0.52; P < .01). Older age (OR, 0.96; P = .04), higher BMI (OR, 0.93; P 0.01), lower RAPT score (OR, 1.2; P = .04), and later surgery start time (OR, 0.80; P < .01) were associated with inpatient discharge. The inpatient model outperformed the RAPT alone (C-statistic, 0.74 vs 0.62; P < .01).

CONCLUSION

We identified preoperatively determined factors associated with (1) SDD as BMI and allergies and (2) inpatient TKA as age, BMI, RAPT score, and surgery start time. Hospitals, providers, patients, families, and payers can use this information for TKA episode planning.

Development, Reporting, and Evaluation of Clinical Practice Guidelines

Clinical practice parameters have been published with greater frequency by professional societies and groups of experts. These publications run the gamut of practice standards, practice guidelines, consensus statements or practice advisories, position statements, and practice alerts. The definitions of these terms have been clarified in an accompanying article. In this article, we present the criteria for high-quality clinical practice parameters and outline a process for developing them, specifically the Delphi method, which is increasingly being used to build consensus among content experts and stakeholders. Several tools for grading the level of evidence and strength of recommendation are offered and compared. The speciousness of categorizing guidelines as evidence-based or consensus-based will be explained. We examine the recommended checklist for reporting and appraise the tools for evaluating a practice guideline. This article is geared toward developers and reviewers of clinical practice guidelines and consensus statements.

Consistent Definitions of Clinical Practice Guidelines, Consensus Statements, Position Statements, and Practice Alerts

An evidence-based approach to clinical decision-making for optimizing patient care is desirable because it promotes quality of care, improves patient safety, decreases medical errors, and reduces health care costs. Clinical practice recommendations are systematically developed documents regarding best practice for specific clinical management issues, which can assist care providers in their clinical decision-making. However, there is currently wide variation in the terminology used for such clinical practice recommendations. The aim of this article is to provide guidance to authors, reviewers, and editors on the definitions of terms commonly used for clinical practice recommendations. This is intended to improve transparency and clarity regarding the definitions of these terminologies.

Repeated Measures Designs and Analysis of Longitudinal Data: If at First You Do Not Succeed-Try, Try Again

Anesthesia, critical care, perioperative, and pain research often involves study designs in which the same outcome variable is repeatedly measured or observed over time on the same patients. Such repeatedly measured data are referred to as longitudinal data, and longitudinal study designs are commonly used to investigate changes in an outcome over time and to compare these changes among treatment groups. From a statistical perspective, longitudinal studies usually increase the precision of estimated treatment effects, thus increasing the power to detect such effects. Commonly used statistical techniques mostly assume independence of the observations or measurements. However, values repeatedly measured in the same individual will usually be more similar to each other than values of different individuals and ignoring the correlation between repeated measurements may lead to biased estimates as well as invalid P values and confidence intervals. Therefore, appropriate analysis of repeated-measures data requires specific statistical techniques. This tutorial reviews 3 classes of commonly used approaches for the analysis of longitudinal data. The first class uses summary statistics to condense the repeatedly measured information to a single number per subject, thus basically eliminating within-subject repeated measurements and allowing for a straightforward comparison of groups using standard statistical hypothesis tests. The second class is historically popular and comprises the repeated-measures analysis of variance type of analyses. However, strong assumptions that are seldom met in practice and low flexibility limit the usefulness of this approach. The third class comprises modern and flexible regression-based techniques that can be generalized to accommodate a wide range of outcome data including continuous, categorical, and count data. Such methods can be further divided into so-called “population-average statistical models” that focus on the specification of the mean response of the outcome estimated by generalized estimating equations, and “subject-specific models” that allow a full specification of the distribution of the outcome by using random effects to capture within-subject correlations. The choice as to which approach to choose partly depends on the aim of the research and the desired interpretation of the estimated effects (population-average versus subject-specific interpretation). This tutorial discusses aspects of the theoretical background for each technique, and with specific examples of studies published in Anesthesia & Analgesia, demonstrates how these techniques are used in practice.