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OBJECTIVE: Tranexamic acid (TXA) is an FDA-approved antifibrinolytic that is seeing increased popularity in spine surgery owing to its ability to reduce intraoperative blood loss (IOBL) and allogeneic transfusion requirements. The present study aimed to summarize the current literature on these formulations in the context of short-segment instrumented lumbar fusion including ≥ 1-level posterior lumbar interbody fusion (PLIF). METHODS: The PubMed, Cochrane, and Web of Science databases were queried for all full-text English studies evaluating the use of topical TXA (tTXA), systemic TXA (sTXA), or combined tTXA+sTXA in patients undergoing PLIF. The primary endpoints of interest were operative time, IOBL, and total blood loss (TBL); secondary endpoints included venous thromboembolic complication occurrence, and allogeneic and autologous transfusion requirements. Outcomes were compared using random effects. Comparisons were made between the following treatment groups: sTXA, tTXA, and sTXA+tTXA. Given that sTXA is arguably the standard of care in the literature (i.e., the most common route of administration that to this point has been studied the most), the authors compared sTXA versus tTXA and sTXA versus sTXA+tTXA. Study heterogeneity was assessed with the I2 test, and grouped analysis using the Hedge's g test was performed for measurement of effect size. RESULTS: Forty-five articles were identified, of which 17 met the criteria for inclusion with an aggregate of 1008 patients. TXA regimens included sTXA only, tTXA only, and various combinations of sTXA and tTXA. There were no significant differences in operative time, TBL, or postoperative drainage between the sTXA and tTXA groups or between the sTXA and sTXA+tTXA groups. CONCLUSIONS: The present meta-analysis suggested clinical equipoise between isolated sTXA, isolated tTXA, and combinatorial tTXA+sTXA formulations as hemostatic adjuvants/neoadjuvants in short-segment fusion including ≥ 1-level PLIF. Given the theoretically lower venous thromboembolism risk associated with tTXA, additional investigations using large cohorts comparing these two formulations within the posterior fusion population are merited. Although TXA has been shown to be effective, there are insufficient data to support topical or systemic administration as superior within the open PLIF population.
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Purpose: To elucidate risk factors for meibomian gland disease (MGD) and understand associated changes in meibography and in relation to ocular surface disease. Patients and Methods: As part of the standard workup for ocular surface disease at a tertiary academic center, 203 patients received an ocular history and lifestyle questionnaire. The questionnaire included detailed inquiries about ocular health and lifestyle, including makeup use, cosmetic eyelid procedures, screen time, and contact lens habits. Subjects also took the standardized patient evaluation of eye dryness (SPEED) II questionnaire. Meibomian gland (MG) dropout and structural changes were evaluated on meibography and scored by three independent graders using meiboscores. Statistical analysis was conducted to identify significant risk factors associated with MG loss. Results: This retrospective, cross-sectional study included 189 patients (378 eyes) with high-quality images for grading, and the average age was 67 years (77% female). Patients older than 45 years had significantly more dropout than younger patients (p < 0.01). Self-reported eye makeup use did not significantly impact MG loss. Patients with a history of blepharoplasty trended toward higher meiboscores, but the difference was not statistically significant. Self-reported screen time did not affect meiboscores. Contact lens use over 20 years was associated with significant MG loss (p < 0.05). SPEED II scores had no relationship to meiboscores (p = 0.75). Conclusion: Older age is a significant risk factor for MG loss. Any contact lens use over 20 years also impacted MG dropout. Highlighting the incongruence of symptoms to signs, SPEED II scores showed no relationship to the structural integrity of MGs.
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BACKGROUND: As ground-level falls (GLFs) are a significant cause of mortality in elderly patients, field triage plays an essential role in patient outcomes. This research investigates how machine learning algorithms can supplement traditional t-tests to recognize statistically significant patterns in medical data and to aid clinical guidelines. METHODS: This is a retrospective study using data from 715 GLF patients over 75 years old. We first calculated P-values for each recorded factor to determine the factor's significance in contributing to a need for surgery (P < .05 is significant). We then utilized the XGBoost machine learning method to rank contributing factors. We applied SHapley Additive exPlanations (SHAP) values to interpret the feature importance and provide clinical guidance via decision trees. RESULTS: The three most significant P-values when comparing patients with and without surgery are as follows: Glasgow Coma Scale (GCS) (P < .001), no comorbidities (P < .001), and transfer-in (P = .019). The XGBoost algorithm determined that GCS and systolic blood pressure contribute most strongly. The prediction accuracy of these XGBoost results based on the test/train split was 90.3%. DISCUSSION: When compared to P-values, XGBoost provides more robust, detailed results regarding the factors that suggest a need for surgery. This demonstrates the clinical applicability of machine learning algorithms. Paramedics can use resulting decision trees to inform medical decision-making in real time. XGBoost's generalizability power increases with more data and can be tuned to prospectively assist individual hospitals.