Background: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

Background: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

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.

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: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

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: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

Background: Few studies have evaluated the use of automated artificial intelligence (AI)-based pain recognition in postoperative settings or the correlation with pain intensity. In this study, various machine learning (ML)-based models using facial expressions, the analgesia nociception index (ANI), and vital signs were developed to predict postoperative pain intensity, and their performances for predicting severe postoperative pain were compared. Methods: In total, 155 facial expressions from patients who underwent gastrectomy were recorded postoperatively; one blinded anesthesiologist simultaneously recorded the ANI score, vital signs, and patient self-assessed pain intensity based on the 11-point numerical rating scale (NRS). The ML models’ area under the receiver operating characteristic curves (AUROCs) were calculated and compared using DeLong’s test. Results: ML models were constructed using facial expressions, ANI, vital signs, and different combinations of the three datasets. The ML model constructed using facial expressions best predicted an NRS ≥ 7 (AUROC 0.93) followed by the ML model combining facial expressions and vital signs (AUROC 0.84) in the test-set. ML models constructed using combined physiological signals (vital signs, ANI) performed better than models based on individual parameters for predicting NRS ≥ 7, although the AUROCs were inferior to those of the ML model based on facial expressions (all P < 0.050). Among these parameters, absolute and relative ANI had the worst AUROCs (0.69 and 0.68, respectively) for predicting NRS ≥ 7. Conclusions The ML model constructed using facial expressions best predicted severe postoperative pain (NRS ≥ 7) and outperformed models constructed from physiological signals.