An Overview of Commonly Used Data Sources in Observational Research in Anesthesia

Accounting for Healthcare Structures When Measuring Variation in Care

Health services research frequently focuses on variation in the structure, process, and outcomes of clinical care. Robust approaches for detection and attribution of variation are foundational to both quality improvement and outcomes research. Describing care in structured healthcare systems across hospitals in which clinicians work to provide care for patients as a multileveled structure allows the impact of organization on practice and outcome to be ascertained. Mixed-effect statistical models can describe both the partitioning of variation among levels of these structures and by inclusion of explanatory variables the valid estimation of the features of health systems, clinicians, or patients, with observed differences in processes or patient outcomes. In this Readers' Toolbox, the authors describe the rationale for considering healthcare structures when assessing clinical practice, outcomes, and sources of variation. They describe statistical considerations and methods for the estimation of analysis of structured data and assessment of variance.

Data Science and Geriatric Anesthesia Research: Opportunity and Challenges

With an increase in geriatric population undergoing surgical procedures, research focused on enhancing their perioperative outcomes is of paramount importance. Currently, most of the evidence-based medicine protocols are driven by studies concentrating on adults encompassing all adult age groups. Given the alterations in physiology with aging, geriatric patients respond differently to anesthetics and, therefore, require specific research initiatives to further expound on the same. Large databases and the development of sophisticated analytic tools can provide meaningful insights into this. Here, we discuss a few research opportunities and challenges that data scientists face when focusing on geriatric perioperative research.

Racial and Ethnic Disparities in Epidural Blood Patch Utilization Among Obstetric Patients in the United States: A Nationwide Analysis, 2016-2020

BACKGROUND

Racial and ethnic disparities in health care delivery can lead to inadequate peripartum pain management and associated adverse maternal outcomes. An epidural blood patch (EBP) is the definitive treatment for moderate to severe postdural puncture headache (PDPH), a potentially debilitating neuraxial anesthesia complication associated with significant maternal morbidity if undertreated. In this nationwide study, we examine the racial and ethnic disparities in the inpatient utilization of EBP after obstetric PDPH in the United States.

METHODS

In this retrospective observational study, we used the National Inpatient Sample, a nationally representative database of discharge records for inpatient admissions in the United States, from 2016 to 2020. We analyzed delivery hospitalizations of women of childbearing age (15-49 years) diagnosed with PDPH. Adjusting for maternal and hospitalization characteristics as confounders, we used a multilevel mixed-effects logistic regression model to compare the rates of EBP utilization by race and ethnicity. Secondarily, among hospitalizations with an EBP, we examined the association between race and ethnicity and the timing of the EBP procedure.

RESULTS

We analyzed 49,300 delivery hospitalizations with a diagnosis of PDPH. An EBP was performed in 24,075 (48.8%; 95% confidence interval [CI], 47.8%-49.9%) of these hospitalizations. EBP was performed in 52.7% (95% CI, 51.3%-54.1%) of White non-Hispanic patients with PDPH. Compared to White non-Hispanic patients, Black non-Hispanic (adjusted odds ratio [aOR] = 0.69; 99% CI, 0.56-0.84), Hispanic (aOR = 0.80, 99% CI, 0.68-0.95), and Asian or Pacific Islander patients (aOR = 0.74, 99% CI, 0.58-0.96) were less likely to receive an EBP. The median (interquartile range [IQR]) time to perform an EBP was 2 (1-3) days after admission, with 90% of EBP procedures completed within 4 days of admission. There was no significant association between race and ethnicity and the timing of EBP placement.

CONCLUSIONS

In this nationwide analysis of delivery hospitalizations from 2016 to 2020 in the United States with a diagnosis of PDPH, we identified racial and ethnic disparities in the utilization of EBP. Minoritized patients identified as Black non-Hispanic, Hispanic, or Asian or Pacific Islander were less likely to receive an EBP for the treatment of PDPH compared to White non-Hispanic patients. Suboptimal treatment of PDPH may be associated with adverse long-term outcomes such as postpartum depression, posttraumatic stress disorder, and chronic headaches. Racial and ethnic disparities in EBP utilization should be further investigated to ensure equitable health care delivery.

Cannabis use disorder and perioperative outcomes following complex cancer surgery

INTRODUCTION

Cannabis usage is increasing in the United States, especially among patients with cancer. We sought to evaluate whether cannabis use disorder (CUD) was associated with higher morbidity and mortality among patients undergoing complex cancer surgery.

METHODS

Patients who underwent complex cancer surgery between January 2016 and December 2019 were identified in the National Inpatient Sample database. CUD was defined according to ICD-10 codes. Propensity score matching was performed to create a 1:1 matched cohort that was well balanced with respect to covariates, which included patient comorbidities, sociodemographic factors, and procedure type. The primary composite outcome was in-hospital mortality and seven major perioperative complications (myocardial ischemia, acute kidney injury, stroke, respiratory failure, venous thromboembolism, hospital-acquired infection, and surgical procedure-related complications).

RESULTS

Among 15 014 patients who underwent a high-risk surgical procedure, a cohort of 7507 patients with CUD (median age; 43 years [IQR: 30-56 years]; n = 3078 [41.0%] female) were matched with 7507 patients who were not cannabis users (median age; 44 years [IQR: 30-58 years); n = 2997 [39.9%] female). CUD was associated with slight increased risk relative to postoperative kidney injury (CUD, 7.8% vs. no CUD, 6.1%); however, in-hospital mortality was slightly lower (CUD, 0.9% vs. no CUD, 1.6%) (both p < 0.001). On multivariable analysis, after controlling for other risk factors, CUD was not associated with higher morbidity and mortality (adjusted odds ratio: 1.06, 95% CI: 0.98-1.15; p = 0.158).

CONCLUSION

CUD was not associated with a higher risk of postoperative morbidity and mortality following complex cancer surgery.

Harnessing Big Data in Critical Care: Exploring a new European Dataset

Freely available datasets have become an invaluable tool to propel data-driven research, especially in the field of critical care medicine. However, the number of datasets available is limited. This leads to the repeated reuse of datasets, inherently increasing the risk of selection bias. Additionally, the need arose to validate insights derived from one dataset with another. In 2023, the Salzburg Intensive Care database (SICdb) was introduced. SICdb offers insights in currently 27,386 intensive care admissions from 21,583 patients. It contains cases of general and surgical intensive care from all disciplines. Amongst others SICdb contains information about: diagnosis, therapies (including data on preceding surgeries), scoring, laboratory values, respiratory and vital signals, and configuration data. Data for SICdb (1.0.6) was collected at one single tertiary care institution of the Department of Anesthesiology and Intensive Care Medicine at the Salzburger Landesklinik (SALK) and Paracelsus Medical University (PMU) between 2013 and 2021. This article aims to elucidate on the characteristics of the dataset, the technical implementation, and provides analysis of its strengths and limitations.

Data Science and Geriatric Anesthesia Research: Opportunity and Challenges

With an increase in geriatric population undergoing surgical procedures, research focused on enhancing their perioperative outcomes is of paramount importance. Currently, most of the evidence-based medicine protocols are driven by studies concentrating on adults encompassing all adult age groups. Given the alterations in physiology with aging, geriatric patients respond differently to anesthetics and, therefore, require specific research initiatives to further expound on the same. Large databases and the development of sophisticated analytic tools can provide meaningful insights into this. Here, we discuss a few research opportunities and challenges that data scientists face when focusing on geriatric perioperative research.

Cannabis Use Disorder and Perioperative Complications

Importance

Cannabis use is growing in the US and is increasingly perceived as harmless. However, the perioperative impact of cannabis use remains uncertain.

Objective

To assess whether cannabis use disorder is associated with increased morbidity and mortality after major elective, inpatient, noncardiac surgery.

Design, Setting, and Participants

This retrospective, population-based, matched cohort study used data from the National Inpatient Sample for adult patients aged 18 to 65 years who underwent major elective inpatient surgery (including cholecystectomy, colectomy, inguinal hernia repair, femoral hernia repair, mastectomy, lumpectomy, hip arthroplasty, knee arthroplasty, hysterectomy, spinal fusion, and vertebral discectomy) from January 2016 to December 2019. Data were analyzed from February to August 2022.

Exposure

Cannabis use disorder, as defined by the presence of specific International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes.

Main Outcome and Measures

The primary composite outcome was in-hospital mortality and 7 major perioperative complications (myocardial ischemia, acute kidney injury, stroke, respiratory failure, venous thromboembolism, hospital-acquired infection, and surgical procedure-related complications) based on ICD-10 discharge diagnosis codes. Propensity score matching was performed to create a 1:1 matched cohort that was well balanced with respect to covariates, which included patient comorbidities, sociodemographic factors, and procedure type.

Results

Among 12 422 hospitalizations, a cohort of 6211 patients with cannabis use disorder (median age, 53 years [IQR, 44-59 years]; 3498 [56.32%] male) were matched with 6211 patients without cannabis use disorder for analysis. Cannabis use disorder was associated with an increased risk of perioperative morbidity and mortality compared with hospitalizations without cannabis use disorder in adjusted analysis (adjusted odds ratio, 1.19; 95% CI, 1.04-1.37; P = .01). The outcome occurred more frequently in the group with cannabis use disorder (480 [7.73%]) compared with the unexposed group (408 [6.57%]).

Conclusions and Relevance

In this cohort study, cannabis use disorder was associated with a modest increased risk of perioperative morbidity and mortality after major elective, inpatient, noncardiac surgery. In the context of increasing cannabis use rates, our findings support preoperative screening for cannabis use disorder as a component of perioperative risk stratification. However, further research is needed to quantify the perioperative impact of cannabis use by route and dosage and to inform recommendations for preoperative cannabis cessation.

Transgender and Nonbinary Patients and Perioperative Scoring Systems: It Is Time for Inclusion

This Viewpoint advocates for inclusion of nonbinary and transgender reporting in medical research and practice.

On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology

Purpose of Review

The purpose of this review is to summarize the current research and critically examine artificial intelligence (AI) technologies and their applicability to the daily practice of anesthesiologists.

Recent Findings

Novel AI tools are developed using data from electronic health records, imaging, waveforms, clinical notes, and wearables. These tools can accurately predict the perioperative risk for adverse outcomes, the need for blood transfusion, and the risk of difficult intubation. Intraoperatively, AI models can assist with technical skill augmentation, patient monitoring, and management. Postoperatively, AI technology can aid in preventing complications and discharge planning. While further prospective validation is needed, these early applications demonstrate promise in every area of perioperative care.

Summary

The practice of anesthesiology is at a precipice fueled by technological innovation. The clinical AI implementation would enable personalized and safer patient care by offering actionable insights from the wealth of perioperative data.

Development and implementation of databases to track patient and safety outcomes

PURPOSE OF REVIEW

Recent advancements in big data analytical tools and large patient databases have expanded tremendously the opportunities to track patient and safety outcomes.We discuss the strengths and limitations of large databases and implementation in practice with a focus on the current opportunities to use technological advancements to improve patient safety.

RECENT FINDINGS

The most used sources of data for large patient safety observational studies are administrative databases, clinical registries, and electronic health records. These data sources have enabled research on patient safety topics ranging from rare adverse outcomes to large cohort studies of the modalities for pain control and safety of medications. Implementing the insights from big perioperative data research is augmented by automating data collection and tracking the safety outcomes on a provider, institutional, national, and global level. In the near future, big data from wearable devices, physiological waveforms, and genomics may lead to the development of personalized outcome measures.

SUMMARY

Patient safety research using large databases can provide actionable insights to improve outcomes in the perioperative setting. As datasets and methods to gain insights from those continue to grow, adopting novel technologies to implement personalized quality assurance initiatives can significantly improve patient care.