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BackgroundPostoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients.Research questionCan a predictive model be developed to accurately identify patients at high risk of PRF?Study design and methodsIn this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores.ResultsTwo hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203).InterpretationThis single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.

Treatment of malignant pancreatic and/or biliary strictures requires tissue diagnosis. Since cytologic brushings at endoscopic retrograde cholangiopancreatography (ERCP) of these strictures has a poor sensitivity for malignancy (30-83%) (see ME Ryan. Gastrointestinal Endoscopy 1991;37(2):139-143; and MB Cohen, Wittchow RJ, Johlin FC, et al. Mod Pathol 1995;8:498-502), tissue diagnosis must be obtained by another route. We report our experience of percutaneous biopsy of malignant pancreatic and/or biliary strictures even when no radiographic mass is present.At ERCP, five patients demonstrated pancreatic and/or biliary duct strictures, had atypical cytological brushings, and had their strictures stented. No mass to account for the strictures could be identified on CT. These five patients underwent percutaneous biopsy of the persistent material by CT within 10 days of the ERCP.Two patients had adenocarcinoma of the pancreas. One patient had malignant lymphoma and another had cholangiocarcinoma. One patient had inflammatory cells and was followed.If the diagnosis of malignancy cannot be made at the time of the ERCP sampling, then our experience suggests that a percutaneous biopsy should be performed even if a mass is not present using the stent as a target.

Background: With the growing prevalence of lumbar spinal stenosis, endoscopic surgery, which incorporates techniques such as transforaminal, interlaminar, and unilateral biportal (UBE) endoscopy, is increasingly considered. However, the patient selection criteria are debated among spine surgeons. Objective: This study used a polytomous Rasch analysis to evaluate the factors influencing surgeon decision-making in selecting patients for endoscopic surgical treatment of lumbar spinal stenosis. Methods: A comprehensive survey was distributed to a representative sample of 296 spine surgeons. Questions encompassed various patient-related and clinical factors, and responses were captured on a logit scale graphically displaying person-item maps and category probability curves for each test item. Using a Rasch analysis, the data were subsequently analyzed to determine the latent traits influencing decision-making. Results: The Rasch analysis revealed that surgeons' preferences for transforaminal, interlaminar, and UBE techniques were easily influenced by comfort level and experience with the endoscopic procedure and patient-related factors. Harder-to-agree items included technological aspects, favorable clinical outcomes, and postoperative functional recovery and rehabilitation. Descriptive statistics suggested interlaminar as the best endoscopic spinal stenosis decompression technique. However, logit person-item analysis integral to the Rasch methodology showed highest intensity for transforaminal followed by interlaminar endoscopic lumbar stenosis decompression. The UBE technique was the hardest to agree on with a disordered person-item analysis and thresholds in category probability curve plots. Conclusion: Surgeon decision-making in selecting patients for endoscopic surgery for lumbar spinal stenosis is multifaceted. While the framework of clinical guidelines remains paramount, on-the-ground experience-based factors significantly influence surgeons' selection of patients for endoscopic lumbar spinal stenosis surgeries. The Rasch methodology allows for a more granular psychometric evaluation of surgeon decision-making and accounts better for years-long experience that may be lost in standardized clinical guideline development. This new approach to assessing spine surgeons' thought processes may improve the implementation of evidence-based protocol change dictated by technological advances was endorsed by the Interamerican Society for Minimally Invasive Spine Surgery (SICCMI), the International Society for Minimal Intervention in Spinal Surgery (ISMISS), the Mexican Spine Society (AMCICO), the Brazilian Spine Society (SBC), the Society for Minimally Invasive Spine Surgery (SMISS), the Korean Minimally Invasive Spine Society (KOMISS), and the International Society for the Advancement of Spine Surgery (ISASS). [ABSTRACT FROM AUTHOR]

Proving clinical superiority of personalized care models in interventional and surgical pain management is challenging. The apparent difficulties may arise from the inability to standardize complex surgical procedures that often involve multiple steps. Ensuring the surgery is performed the same way every time is nearly impossible. Confounding factors, such as the variability of the patient population and selection bias regarding comorbidities and anatomical variations are also difficult to control for. Small sample sizes in study groups comparing iterations of a surgical protocol may amplify bias. It is essentially impossible to conceal the surgical treatment from the surgeon and the operating team. Restrictive inclusion and exclusion criteria may distort the study population to no longer reflect patients seen in daily practice. Hindsight bias is introduced by the inability to effectively blind patient group allocation, which affects clinical result interpretation, particularly if the outcome is already known to the investigators when the outcome analysis is performed (often a long time after the intervention). Randomization is equally problematic, as many patients want to avoid being randomly assigned to a study group, particularly if they perceive their surgeon to be unsure of which treatment will likely render the best clinical outcome for them. Ethical concerns may also exist if the study involves additional and unnecessary risks. Lastly, surgical trials are costly, especially if the tested interventions are complex and require long-term follow-up to assess their benefit. Traditional clinical testing of personalized surgical pain management treatments may be more challenging because individualized solutions tailored to each patient's pain generator can vary extensively. However, high-grade evidence is needed to prompt a protocol change and break with traditional image-based criteria for treatment. In this article, the authors review issues in surgical trials and offer practical solutions. [ABSTRACT FROM AUTHOR]