Articles: surgery.
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Moderate traumatic brain injury (TBI) is a diagnosis that describes diverse patients with heterogeneity of primary injuries. Defined by a Glasgow Coma Scale between 9 and 12, this category includes patients who may neurologically worsen and require increasing intensive care resources and/or emergency neurosurgery. Despite the unique characteristics of these patients, there have not been specific guidelines published before this effort to support decision-making in these patients. ⋯ Moderate TBI is an entity for which there is little published evidence available supporting definition, diagnosis, and management. Recommendations based on experts' opinion were informed by available evidence and aim to refine the definition and categorization of moderate TBI. Further studies evaluating the impact of these recommendations will be required.
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Neurosurgeons and hospitals devote tremendous resources to improving recovery from lumbar spine surgery. Current efforts to predict surgical recovery rely on one-time patient report and health record information. However, longitudinal mobile health (mHealth) assessments integrating symptom dynamics from ecological momentary assessment (EMA) and wearable biometric data may capture important influences on recovery. Our objective was to evaluate whether a preoperative mHealth assessment integrating EMA with Fitbit monitoring improved predictions of spine surgery recovery. ⋯ Multimodal mHealth evaluations improve predictions of lumbar surgery outcomes. These methods may be useful for informing patient selection and perioperative recovery strategies.
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Arginine vasopressin (AVP) is an important hormone responsible for maintaining sodium homeostasis after pituitary surgery. The measurement of AVP levels is difficult because of its short half-life (t 1/2 ). Copeptin is a preprohormone of AVP, and it is a more stable peptide, which can be used as surrogate marker for AVP. This study aims to assess the role of copeptin as a predictor of postoperative hyponatremia and hypernatremia in patients undergoing endoscopic pituitary adenoma surgery. ⋯ A relative increase or decrease in early change in copeptin (C2-C1) can predict development of early hyponatremia or transient central diabetes insipidus, respectively. A relative increase in delayed change in copeptin (C3-C1) can predict development of delayed hyponatremia.
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Observational studies of anesthetic neurotoxicity may be biased because children requiring anesthesia commonly have medical conditions associated with neurobehavioral problems. This study takes advantage of a natural experiment associated with appendicitis to determine whether anesthesia and surgery in childhood were specifically associated with subsequent neurobehavioral outcomes. ⋯ Although there is an association between neurobehavioral diagnoses and appendectomy, this association is not specific to anesthesia exposure and is stronger in medical admissions. Medical admissions, generally without anesthesia exposure, displayed significantly higher rates of these disorders than appendectomy-exposed patients.
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Anesthesia and analgesia · Sep 2024
Decision Curve Analysis of In-Hospital Mortality Prediction Models: The Relative Value of Pre- and Intraoperative Data For Decision-Making.
Clinical prediction modeling plays a pivotal part in modern clinical care, particularly in predicting the risk of in-hospital mortality. Recent modeling efforts have focused on leveraging intraoperative data sources to improve model performance. However, the individual and collective benefit of pre- and intraoperative data for clinical decision-making remains unknown. We hypothesized that pre- and intraoperative predictors contribute equally to the net benefit in a decision curve analysis (DCA) of in-hospital mortality prediction models that include pre- and intraoperative predictors. ⋯ When it comes to predicting in-hospital mortality and subsequent decision-making, preoperative demographics, comorbidities, and surgery-related data provide the largest benefit for clinicians with risk-averse preferences, whereas preoperative laboratory values provide the largest benefit for decision-makers with more moderate risk preferences. Our decision-analytic investigation of different predictor categories moves beyond the question of whether certain predictors provide a benefit in traditional performance metrics (eg, AUROC). It offers a nuanced perspective on for whom these predictors might be beneficial in clinical decision-making. Follow-up studies requiring larger datasets and dedicated deep-learning models to handle continuous intraoperative data are essential to examine the robustness of our results.