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Munir MM, Woldesenbet S, Endo Y, Dillhoff M, Pawlik TM. Cannabis use disorder and perioperative outcomes following complex cancer surgery. J Surg Oncol 2024; 129:1430-1441. [PMID: 38606521 DOI: 10.1002/jso.27644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/13/2024]
Abstract
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.
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Affiliation(s)
- Muhammad M Munir
- Department of Surgery, Wexner Medical Center and James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA
| | - Selamawit Woldesenbet
- Department of Surgery, Wexner Medical Center and James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA
| | - Yutaka Endo
- Department of Surgery, Wexner Medical Center and James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA
| | - Mary Dillhoff
- Department of Surgery, Wexner Medical Center and James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA
| | - Timothy M Pawlik
- Department of Surgery, Wexner Medical Center and James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA
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Rodemund N, Wernly B, Jung C, Cozowicz C, Koköfer A. Harnessing Big Data in Critical Care: Exploring a new European Dataset. Sci Data 2024; 11:320. [PMID: 38548745 PMCID: PMC10978926 DOI: 10.1038/s41597-024-03164-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 03/01/2024] [Indexed: 04/01/2024] Open
Abstract
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.
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Affiliation(s)
- Niklas Rodemund
- Department of Anesthesiology, Perioperative Medicine and Intensive Care Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Bernhard Wernly
- Department of Internal Medicine, General Hospital Oberndorf, Teaching Hospital of the Paracelsus Medical University Salzburg, Oberndorf, Austria
- Center for Public Health and Healthcare Research, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Christian Jung
- Division of Cardiology, Pulmonary Diseases, Vascular Medicine Medical Faculty, University Dusseldorf, University Hospital Dusseldorf, Dusseldorf, Germany
| | - Crispiana Cozowicz
- Department of Anesthesiology, Perioperative Medicine and Intensive Care Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Andreas Koköfer
- Department of Anesthesiology, Perioperative Medicine and Intensive Care Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria.
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Sandhu MRS, Tickoo M, Bardia A. Data Science and Geriatric Anesthesia Research: Opportunity and Challenges. Anesthesiol Clin 2023; 41:631-646. [PMID: 37516499 DOI: 10.1016/j.anclin.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2023]
Abstract
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.
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Affiliation(s)
- Mani Ratnesh S Sandhu
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Mayanka Tickoo
- Division of Pulmonary, Department of Medicine, Critical Care and Sleep Medicine, Tufts Medical Center, Biewend Building, 3Road Floor, 260 Tremont Street, Boston, MA 02118, USA
| | - Amit Bardia
- Department of Anesthesiology, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 06520, USA.
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Potnuru PP, Jonna S, Williams GW. Cannabis Use Disorder and Perioperative Complications. JAMA Surg 2023; 158:935-944. [PMID: 37405729 PMCID: PMC10323761 DOI: 10.1001/jamasurg.2023.2403] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/19/2023] [Indexed: 07/06/2023]
Abstract
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.
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Affiliation(s)
- Paul P. Potnuru
- Department of Anesthesiology, Critical Care and Pain Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston
| | - Srikar Jonna
- Department of Anesthesiology, Critical Care and Pain Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston
| | - George W. Williams
- Department of Anesthesiology, Critical Care and Pain Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston
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Davoud SC, Kovacheva VP. On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology. CURRENT ANESTHESIOLOGY REPORTS 2023; 13:31-40. [PMID: 38106626 PMCID: PMC10722862 DOI: 10.1007/s40140-023-00558-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2023] [Indexed: 04/08/2023]
Abstract
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.
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Affiliation(s)
- Sherwin C. Davoud
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., L1, Boston, MA, USA
| | - Vesela P. Kovacheva
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., L1, Boston, MA, USA
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Sideris A, Zhong H, Liu J, Poeran J, Memtsoudis SG. Outpatient prescription cannabinoid utilisation in the USA: a population-based study. Br J Anaesth 2023; 130:e406-e408. [PMID: 36566126 DOI: 10.1016/j.bja.2022.11.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Alexandra Sideris
- Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery, New York, NY, USA; Department of Anesthesiology, Weill Cornell Medical College, New York, NY, USA; HSS Research Institute, Hospital for Special Surgery, New York, NY, USA
| | - Haoyan Zhong
- Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery, New York, NY, USA
| | - Jiabin Liu
- Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery, New York, NY, USA; Department of Anesthesiology, Weill Cornell Medical College, New York, NY, USA
| | - Jashvant Poeran
- Institute for Healthcare Delivery Science, Department of Population Health Science & Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stavros G Memtsoudis
- Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery, New York, NY, USA; Department of Anesthesiology, Weill Cornell Medical College, New York, NY, USA; HSS Research Institute, Hospital for Special Surgery, New York, NY, USA; Department of Health Policy and Research, Weill Cornell Medical College, New York, NY, USA; Department of Anesthesiology, Perioperative Medicine and Intensive Care Medicine, Paracelsus Medical Private University, Salzburg, Austria.
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Mukasa CDM, Kovacheva VP. Development and implementation of databases to track patient and safety outcomes. Curr Opin Anaesthesiol 2022; 35:710-716. [PMID: 36302209 PMCID: PMC10262595 DOI: 10.1097/aco.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
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.
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Affiliation(s)
- Christopher D M Mukasa
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Williams GW, Rihani R, Bui A. HCUP Databases May Be Helpful in Limiting Bias. Anesth Analg 2022; 135:e21. [DOI: 10.1213/ane.0000000000006107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Closing the Knowledge Translation Gap: Health Services Research and Perioperative Medicine-New Horizons for Anesthesiologists. Anesth Analg 2022; 134:441-443. [PMID: 35180157 DOI: 10.1213/ane.0000000000005892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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