Value assignment and uncertainty evaluation for anion and single-element reference solutions incorporating historical information.

The National Institute of Standards and Technology, which is the national metrology institute of the USA, assigns certified values to the mass fractions of individual elements in single-element solutions, and to the mass fractions of anions in anion solutions, based on gravimetric preparations and instrumental methods of analysis. The instrumental method currently is high-performance inductively coupled plasma optical emission spectroscopy for the single-element solutions, and ion chromatography for the anion solutions. The uncertainty associated with each certified value comprises method-specific components, a component reflecting potential long-term instability that may affect the certified mass fraction during the useful lifetime of the solutions, and a component from between-method differences. Lately, the latter has been evaluated based only on the measurement results for the reference material being certified. The new procedure described in this contribution blends historical information about between-method differences for similar solutions produced previously, with the between-method difference observed when a new material is characterized. This blending procedure is justified because, with only rare exceptions, the same preparation and measurement methods have been used historically: in the course of almost 40 years for the preparation methods, and of 20 years for the instrumental methods. Also, the certified values of mass fraction, and the associated uncertainties, have been very similar, and the chemistry of the solutions also is closely comparable within each series of materials. If the new procedure will be applied to future SRM lots of single-element or anion solutions routinely, then it is expected that it will yield relative expanded uncertainties that are about 20 % smaller than the procedure for uncertainty evaluation currently in use, and that it will do so for the large majority of the solutions. However, more consequential than any reduction in uncertainty, is the improvement in the quality of the uncertainty evaluations that derives from incorporating the rich historical information about between-method differences and about the stability of the solutions over their expected lifetimes. The particular values listed for several existing SRMs are given merely as retrospective illustrations of the application of the new method, not to suggest that the certified values or their associated uncertainties should be revised.

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.

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.

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.

Narrative Medicine: Perioperative Opportunities and Applicable Health Services Research Methods.

Narrative medicine is a humanities-based discipline that posits that attention to the patient narrative and the collaborative formation of a narrative between the patient and provider is essential for the provision of health care. In this Special Article, we review the basic theoretical constructs of the narrative medicine discipline and apply them to the perioperative setting. We frame our discussion around the 4 primary goals of the current iteration of the perioperative surgical home: enhancing patient-centered care, embracing shared decision making, optimizing health literacy, and avoiding futile surgery. We then examine the importance of incorporating narrative medicine into medical education and residency training and evaluate the literature on such narrative medicine didactics. Finally, we discuss applying health services research, specifically qualitative and mixed methods, in the rigorous evaluation of the efficacy and impact of narrative medicine clinical programs and medical education curricula.

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.

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.

Systematic Review and Meta-analysis: Sometimes Bigger Is Indeed Better.

Clinicians encounter an ever increasing and frequently overwhelming amount of information, even in a narrow scope or area of interest. Given this enormous amount of scientific information published every year, systematic reviews and meta-analyses have become indispensable methods for the evaluation of medical treatments and the delivery of evidence-based best practice. The present basic statistical tutorial thus focuses on the fundamentals of a systematic review and meta-analysis, against the backdrop of practicing evidence-based medicine. Even if properly performed, a single study is no more than tentative evidence, which needs to be confirmed by additional, independent research. A systematic review summarizes the existing, published research on a particular topic, in a well-described, methodical, rigorous, and reproducible (hence "systematic") manner. A systematic review typically includes a greater range of patients than any single study, thus strengthening the external validity or generalizability of its findings and the utility to the clinician seeking to practice evidence-based medicine. A systematic review often forms the basis for a concomitant meta-analysis, in which the results from the identified series of separate studies are aggregated and statistical pooling is performed. This allows for a single best estimate of the effect or association. A conjoint systematic review and meta-analysis can provide an estimate of therapeutic efficacy, prognosis, or diagnostic test accuracy. By aggregating and pooling the data derived from a systemic review, a well-done meta-analysis essentially increases the precision and the certainty of the statistical inference. The resulting single best estimate of effect or association facilitates clinical decision making and practicing evidence-based medicine. A well-designed systematic review and meta-analysis can provide valuable information for researchers, policymakers, and clinicians. However, there are many critical caveats in performing and interpreting them, and thus, like the individual research studies on which they are based, there are many ways in which meta-analyses can yield misleading information. Creators, reviewers, and consumers alike of systematic reviews and meta-analyses would thus be well-served to observe and mitigate their associated caveats and potential pitfalls.

Psychometrics: Trust, but Verify.

There is a continued mandate for practicing evidence-based medicine and the prerequisite rigorous analysis of the comparative effectiveness of alternative treatments. There is also an increasing emphasis on delivering value-based health care. Both these high priorities and their related endeavors require correct information about the outcomes of care. Accurately measuring and confirming health care outcomes are thus likely now of even greater importance. The present basic statistical tutorial focuses on the germane topic of psychometrics. In its narrower sense, psychometrics is the science of evaluating the attributes of such psychological tests. However, in its broader sense, psychometrics is concerned with the objective measurement of the skills, knowledge, and abilities, as well as the subjective measurement of the interests, values, and attitudes of individuals-both patients and their clinicians. While psychometrics is principally the domain and content expertise of psychiatry, psychology, and social work, it is also very pertinent to patient care, education, and research in anesthesiology, perioperative medicine, critical care, and pain medicine. A key step in selecting an existing or creating a new health-related assessment tool, scale, or survey is confirming or establishing the usefulness of the existing or new measure; this process conventionally involves assessing its reliability and its validity. Assessing reliability involves demonstrating that the measurement instrument generates consistent and hence reproducible results-in other words, whether the instrument produces the same results each time it is used in the same setting, with the same type of subjects. This includes interrater reliability, intrarater reliability, test-retest reliability, and internal reliability. Assessing validity is answering whether the instrument is actually measuring what it is intended to measure. This includes content validity, criterion validity, and construct validity. In evaluating a reported set of research data and its analyses, in a similar manner, it is important to assess the overall internal validity of the attendant study design and the external validity (generalizability) of its findings.

Statistical Process Control: No Hits, No Runs, No Errors?

A novel intervention or new clinical program must achieve and sustain its operational and clinical goals. To demonstrate successfully optimizing health care value, providers and other stakeholders must longitudinally measure and report these tracked relevant associated outcomes. This includes clinicians and perioperative health services researchers who chose to participate in these process improvement and quality improvement efforts ("play in this space"). Statistical process control is a branch of statistics that combines rigorous sequential, time-based analysis methods with graphical presentation of performance and quality data. Statistical process control and its primary tool-the control chart-provide researchers and practitioners with a method of better understanding and communicating data from health care performance and quality improvement efforts. Statistical process control presents performance and quality data in a format that is typically more understandable to practicing clinicians, administrators, and health care decision makers and often more readily generates actionable insights and conclusions. Health care quality improvement is predicated on statistical process control. Undertaking, achieving, and reporting continuous quality improvement in anesthesiology, critical care, perioperative medicine, and acute and chronic pain management all fundamentally rely on applying statistical process control methods and tools. Thus, the present basic statistical tutorial focuses on the germane topic of statistical process control, including random (common) causes of variation versus assignable (special) causes of variation: Six Sigma versus Lean versus Lean Six Sigma, levels of quality management, run chart, control charts, selecting the applicable type of control chart, and analyzing a control chart. Specific attention is focused on quasi-experimental study designs, which are particularly applicable to process improvement and quality improvement efforts.

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.

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.

Survival Analysis and Interpretation of Time-to-Event Data: The Tortoise and the Hare.

Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzing the length of time until the occurrence of a well-defined end point of interest. A unique feature of survival data is that typically not all patients experience the event (eg, death) by the end of the observation period, so the actual survival times for some patients are unknown. This phenomenon, referred to as censoring, must be accounted for in the analysis to allow for valid inferences. Moreover, survival times are usually skewed, limiting the usefulness of analysis methods that assume a normal data distribution. As part of the ongoing series in Anesthesia & Analgesia, this tutorial reviews statistical methods for the appropriate analysis of time-to-event data, including nonparametric and semiparametric methods-specifically the Kaplan-Meier estimator, log-rank test, and Cox proportional hazards model. These methods are by far the most commonly used techniques for such data in medical literature. Illustrative examples from studies published in Anesthesia & Analgesia demonstrate how these techniques are used in practice. Full parametric models and models to deal with special circumstances, such as recurrent events models, competing risks models, and frailty models, are briefly discussed.