Purpose: Compassion is integral to nursing, yet the concept of compassionate care is not thoroughly understood. This study aimed to clarify the nature of compassionate care among Korean clinical nurses using a hybrid model. Methods: This study utilized a mixed methods approach involving hybrid concept analysis to explore the nature and attributes of compassionate care, encompassing both theoretical and empirical stages. In the theoretical stage, the domains, attributes, and a preliminary definition of compassionate care were formulated. In the fieldwork stage, in-depth interviews with 18 nurses were conducted to gather insights, which were integrated in the final stage. Results: Compassionate care was categorized into three domains: cognitive, relational, and behavioral, with 11 defining attributes. It was defined as the capacity to recognize individual patient needs, engage empathetically with their suffering, establish trust through emotional connection, and deliver therapeutic partnership, specialized, personalized, ethical, and holistic care. Conclusion This study advances the understanding of compassionate care in nursing by providing a multidimensional framework. It lays the groundwork for future research and practical applications, emphasizing the need for measurement tools and strategies to promote compassionate care among clinical nurses.

Background: Maintenance of stable blood pressure (BP) during cerebrovascular bypass surgery is crucial to prevent cerebral ischemia. We compared the effect of remimazolam anesthesia with that of propofol-induced and desflurane-maintained anesthesia on intraoperative hemodynamic stability and the need for vasoactive agents in patients undergoing cerebrovascular bypass surgery. Methods: Sixty-five patients were randomized into remimazolam (n = 31, remimazolam-based intravenous anesthesia) and control groups (n = 34, propofol-induced and desflurane-maintained anesthesia). The primary outcome was the occurrence of intraoperative hypotension. The secondary outcomes included hypotension duration, lowest mean BP (MBP), generalized average real variability (ARV) of MBP, and consumption of phenylephrine, norepinephrine, or remifentanil. Results: Occurrence rate and duration of hypotension were significantly lower in the remimazolam group (38.7% vs. 73.5%, P = 0.005; 0 [0, 10] vs. 7.5 [1.25, 25] min, P = 0.008). Remimazolam also showed better outcomes for lowest MBP (78 [73, 84] vs. 69.5 [66.25, 75.8] mmHg, P < 0.001) and generalized ARV of MBP (1.42 ± 0.49 vs. 1.66 ± 0.52 mmHg/min, P = 0.036). The remimazolam group required less phenylephrine (20 [0, 65] vs. 100 [60, 130] μg, P < 0.001), less norepinephrine (162 [0, 365.5] vs. 1335 [998.5, 1637.5] μg, P < 0.001), and more remifentanil (1750 [1454.5, 2184.5] vs. 531 [431, 746.5] μg, P < 0.001) than the control group. Conclusions Remimazolam anesthesia may provide better hemodynamic stability during cerebrovascular bypass surgery than propofol-induced and desflurane-maintained anesthesia.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Background: Maintenance of stable blood pressure (BP) during cerebrovascular bypass surgery is crucial to prevent cerebral ischemia. We compared the effect of remimazolam anesthesia with that of propofol-induced and desflurane-maintained anesthesia on intraoperative hemodynamic stability and the need for vasoactive agents in patients undergoing cerebrovascular bypass surgery. Methods: Sixty-five patients were randomized into remimazolam (n = 31, remimazolam-based intravenous anesthesia) and control groups (n = 34, propofol-induced and desflurane-maintained anesthesia). The primary outcome was the occurrence of intraoperative hypotension. The secondary outcomes included hypotension duration, lowest mean BP (MBP), generalized average real variability (ARV) of MBP, and consumption of phenylephrine, norepinephrine, or remifentanil. Results: Occurrence rate and duration of hypotension were significantly lower in the remimazolam group (38.7% vs. 73.5%, P = 0.005; 0 [0, 10] vs. 7.5 [1.25, 25] min, P = 0.008). Remimazolam also showed better outcomes for lowest MBP (78 [73, 84] vs. 69.5 [66.25, 75.8] mmHg, P < 0.001) and generalized ARV of MBP (1.42 ± 0.49 vs. 1.66 ± 0.52 mmHg/min, P = 0.036). The remimazolam group required less phenylephrine (20 [0, 65] vs. 100 [60, 130] μg, P < 0.001), less norepinephrine (162 [0, 365.5] vs. 1335 [998.5, 1637.5] μg, P < 0.001), and more remifentanil (1750 [1454.5, 2184.5] vs. 531 [431, 746.5] μg, P < 0.001) than the control group. Conclusions Remimazolam anesthesia may provide better hemodynamic stability during cerebrovascular bypass surgery than propofol-induced and desflurane-maintained anesthesia.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Background: Maintenance of stable blood pressure (BP) during cerebrovascular bypass surgery is crucial to prevent cerebral ischemia. We compared the effect of remimazolam anesthesia with that of propofol-induced and desflurane-maintained anesthesia on intraoperative hemodynamic stability and the need for vasoactive agents in patients undergoing cerebrovascular bypass surgery. Methods: Sixty-five patients were randomized into remimazolam (n = 31, remimazolam-based intravenous anesthesia) and control groups (n = 34, propofol-induced and desflurane-maintained anesthesia). The primary outcome was the occurrence of intraoperative hypotension. The secondary outcomes included hypotension duration, lowest mean BP (MBP), generalized average real variability (ARV) of MBP, and consumption of phenylephrine, norepinephrine, or remifentanil. Results: Occurrence rate and duration of hypotension were significantly lower in the remimazolam group (38.7% vs. 73.5%, P = 0.005; 0 [0, 10] vs. 7.5 [1.25, 25] min, P = 0.008). Remimazolam also showed better outcomes for lowest MBP (78 [73, 84] vs. 69.5 [66.25, 75.8] mmHg, P < 0.001) and generalized ARV of MBP (1.42 ± 0.49 vs. 1.66 ± 0.52 mmHg/min, P = 0.036). The remimazolam group required less phenylephrine (20 [0, 65] vs. 100 [60, 130] μg, P < 0.001), less norepinephrine (162 [0, 365.5] vs. 1335 [998.5, 1637.5] μg, P < 0.001), and more remifentanil (1750 [1454.5, 2184.5] vs. 531 [431, 746.5] μg, P < 0.001) than the control group. Conclusions Remimazolam anesthesia may provide better hemodynamic stability during cerebrovascular bypass surgery than propofol-induced and desflurane-maintained anesthesia.