Convergent Validity of Three Methods for Measuring Postoperative Complications

Fritz, Bradley A.; Escallier, Krisztina E.; Abdallah, Arbi Ben; Oberhaus, Jordan; Becker, J. M.; Geczi, Kristin; McKinnon, Sherry; Helsten, Daniel L.; Sharma, Anshuman; Wildes, Troy S.; Avidan, Michael S.

doi:10.1097/aln.0000000000001108

Cited by 15 publications

(13 citation statements)

References 34 publications

(26 reference statements)

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“…The validity of the outcomes determined by automated health record review has previously been compared with manual record review and patientreported outcomes. 36 Similar to another report, 37 this previous study 36 found large positive likelihood ratios with moderate sensitivity. Another study 38 Funding/Support: The study was supported by the Big Ideas Program, a BJC HealthCare and Washington University School of Medicine internal grant program hosted by the Healthcare Innovation Lab and the Institute for Informatics (Dr Abraham).…”

Section: Limitationssupporting

confidence: 85%

Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications

et al. 2021

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Key Points Question Can machine learning models predict patient risks of postoperative complications related to pneumonia, acute kidney injury, deep vein thrombosis, delirium, and pulmonary embolism? Findings In a cohort study of 111 888 operations at a large academic medical center, machine learning algorithms exhibited high areas under the receiver operating characteristic curve for predicting the risk of postoperative complications related to pneumonia, acute kidney injury, deep vein thrombosis, pulmonary embolism, and delirium. Meaning These findings suggest that machine learning models using preoperative and intraoperative data can predict postoperative complications and generate reliable and clinically meaningful interpretations for supporting clinical decisions along the perioperative care continuum.

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Section: Limitationssupporting

confidence: 85%

Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications

et al. 2021

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“…SATISFY-SOS tracks intermediate-term postoperative outcomes; patients complete postoperative surveys (approximately 1 month and 1 year after surgery) that includes the Veterans Rand 12 Item Health Survey (VR-12), a validated measure of QoL. QoL will be a secondary outcome in this subset of patients; based on our prior work with SATISFY-SOS, 1500 to 2000 responses (versus ~1 00 000 extracted the EHR) will be available for analysis 38. We will not link to delirium or other assessments from independent studies conducted during this period at BJH (ENGAGES, PODCAST).…”

Section: Methodsmentioning

confidence: 99%

Obstructive sleep apnoea, positive airway pressure treatment and postoperative delirium: protocol for a retrospective observational study

King

Escallier

et al. 2019

BMJ Open

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IntroductionObstructive sleep apnoea (OSA) is common among older surgical patients, and delirium is a frequent and serious postoperative complication. Emerging evidence suggests that OSA increases the risk for postoperative delirium. We hypothesise that OSA is an independent risk factor for postoperative delirium, and that in patients with OSA, perioperative adherence to positive airway pressure (PAP) therapy decreases the incidence of postoperative delirium and its sequelae. The proposed retrospective cohort analysis study will use existing datasets to: (i) describe and compare the incidence of postoperative delirium in surgical patients based on OSA diagnosis and treatment with PAP; (ii) assess whether preoperatively untreated OSA is independently associated with postoperative delirium; and (iii) explore whether preoperatively untreated OSA is independently associated with worse postoperative quality of life (QoL). The findings of this study will inform on the potential utility and approach of an interventional trial aimed at preventing postoperative delirium in patients with diagnosed and undiagnosed OSA.Methods and analysisObservational data from existing electronic databases will be used, including over 100 000 surgical patients and ~10 000 intensive care unit (ICU) admissions. We will obtain the incidence of postoperative delirium in adults admitted postoperatively to the ICU who underwent structured preoperative assessment, including OSA diagnosis and screening. We will use doubly robust propensity score methods to assess whether untreated OSA independently predicts postoperative delirium. Using similar methodology, we will assess if untreated OSA independently predicts worse postoperative QoL.Ethics and disseminationThis study has been approved by the Human Research Protection Office at Washington University School of Medicine. We will publish the results in a peer-reviewed venue. Because the data are secondary and high risk for reidentification, we will not publicly share the data. Data will be destroyed after 1 year of completion of active Institutional Review Board (IRB) approved projects.

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“…QoL will be a secondary outcome in this subset of patients; based on our prior work with SATISFY-SOS, 1500-2000 responses (versus ∼100,000 extracted the EHR) will be available for analysis. 38 We will not link to delirium or other assessments from independent studies conducted during this period at BJH (ENGAGES, PODCAST).…”

Section: Methodsmentioning

confidence: 99%

Obstructive sleep apnea, positive airway pressure treatment, and postoperative delirium: protocol for a retrospective observational study

King

Escallier

et al. 2019

Preprint

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Introduction Obstructive sleep apnea (OSA) is common among older surgical patients, and delirium is a frequent and serious postoperative complication. Emerging evidence suggests that OSA increases the risk for postoperative delirium. We hypothesize that OSA is an independent risk factor for postoperative delirium, and that in patients with OSA, perioperative adherence to positive airway pressure (PAP) therapy decreases the incidence of postoperative delirium and its sequelae. The proposed retrospective cohort analysis study will use existing datasets to: (i) describe and compare the incidence of postoperative delirium in surgical patients based on OSA diagnosis and treatment with PAP; (ii) assess whether preoperatively untreated OSA is independently associated with postoperative delirium; and (iii) explore whether preoperatively untreated OSA is independently associated with worse postoperative quality of life. The findings of this study will inform on the potential utility and approach of an interventional trial aimed at preventing postoperative delirium in patients with diagnosed and undiagnosed OSA. Methods and Analysis Observational data from existing electronic databases will be used, including over 100,000 surgical patients and ~10,000 intensive care unit (ICU) admissions. We will obtain the incidence of postoperative delirium in adults admitted postoperatively to the ICU who underwent structured preoperative assessment, including OSA diagnosis and screening. We will use doubly robust propensity score methods to assess whether untreated OSA independently predicts postoperative delirium. Using similar methodology, we will assess if untreated OSA independently predicts worse postoperative quality of life. Ethics and dissemination This study has been approved by the Human Research Protection Office at Washington University School of Medicine. We will publish the results in a peer-reviewed venue. Because the data is secondary and high risk for re-identification, we will not publicly share the data. Data will be destroyed after 1 year of completion of active IRB approved projects.

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Convergent Validity of Three Methods for Measuring Postoperative Complications

Cited by 15 publications

References 34 publications

Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications

Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications

Obstructive sleep apnoea, positive airway pressure treatment and postoperative delirium: protocol for a retrospective observational study

Obstructive sleep apnea, positive airway pressure treatment, and postoperative delirium: protocol for a retrospective observational study

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