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: There are few safe effective ways to relieve osteoarthritis (OA) pain; as a result, off-label psychotropic drug prescriptions have increased worldwide. This study examined the change in psychotropic drug prescriptions for patients with OA from 2011 to 2020 using the Korean National Health Insurance Service dataset. Methods: The study population consisted of patients with hip or knee OA aged ≥ 65 years.Psychotropic drugs included opioids, benzodiazepines, non-benzodiazepine hypnotics (Z-drugs), anti-epileptics, tricyclic antidepressants, selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), typical antipsychotics, atypical antipsychotics, and anxiolytics. The prevalence and long-term (> 3 months) prescription rates of psychotropic drugs in OA patients were calculated. Results: The study included 1,821,158 patients with OA (mean age 71.7 years; 65.32% female).Of the cohort, 49% had comorbidities for which psychotropics were indicated. The prevalence of psychotropic prescriptions decreased from 58.2% to 52.0% in 2018 and then leveled off.The long-term prescription rate remained constant until 2018 and then increased slightly.The most commonly prescribed psychotropics were opioids and long- and short-acting benzodiazepines. The prescription rates of opioids and long-acting benzodiazepines decreased from 2011 to 2020. For those with psychiatric co-morbidities, the prescription rates of anti-epileptics and SNRIs increased, while the prescription rates of anti-epileptics, SSRIs, other antidepressants, and atypical psychotropics increased for those without such co-morbidities. The most commonly prescribed psychotropics were diazepam and alprazolam, excluding tramadol and tramadol–acetaminophen combination. For those with psychiatric co-morbidities, the prescription rates of gabapentin and fentanyl increased, while for those without such co-morbidities, the prescription rates of lorazepam, fentanyl, escitalopram and quetiapine increased. Conclusion A significant number of older Korean patients with OA were prescribed psychotropic drugs in the absence of comorbidities requiring such drugs, including drugs that have little effect on OA and unfavorable safety profiles in older adults.

Background: There are few safe effective ways to relieve osteoarthritis (OA) pain; as a result, off-label psychotropic drug prescriptions have increased worldwide. This study examined the change in psychotropic drug prescriptions for patients with OA from 2011 to 2020 using the Korean National Health Insurance Service dataset. Methods: The study population consisted of patients with hip or knee OA aged ≥ 65 years.Psychotropic drugs included opioids, benzodiazepines, non-benzodiazepine hypnotics (Z-drugs), anti-epileptics, tricyclic antidepressants, selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), typical antipsychotics, atypical antipsychotics, and anxiolytics. The prevalence and long-term (> 3 months) prescription rates of psychotropic drugs in OA patients were calculated. Results: The study included 1,821,158 patients with OA (mean age 71.7 years; 65.32% female).Of the cohort, 49% had comorbidities for which psychotropics were indicated. The prevalence of psychotropic prescriptions decreased from 58.2% to 52.0% in 2018 and then leveled off.The long-term prescription rate remained constant until 2018 and then increased slightly.The most commonly prescribed psychotropics were opioids and long- and short-acting benzodiazepines. The prescription rates of opioids and long-acting benzodiazepines decreased from 2011 to 2020. For those with psychiatric co-morbidities, the prescription rates of anti-epileptics and SNRIs increased, while the prescription rates of anti-epileptics, SSRIs, other antidepressants, and atypical psychotropics increased for those without such co-morbidities. The most commonly prescribed psychotropics were diazepam and alprazolam, excluding tramadol and tramadol–acetaminophen combination. For those with psychiatric co-morbidities, the prescription rates of gabapentin and fentanyl increased, while for those without such co-morbidities, the prescription rates of lorazepam, fentanyl, escitalopram and quetiapine increased. Conclusion A significant number of older Korean patients with OA were prescribed psychotropic drugs in the absence of comorbidities requiring such drugs, including drugs that have little effect on OA and unfavorable safety profiles in older adults.

Background: There are few safe effective ways to relieve osteoarthritis (OA) pain; as a result, off-label psychotropic drug prescriptions have increased worldwide. This study examined the change in psychotropic drug prescriptions for patients with OA from 2011 to 2020 using the Korean National Health Insurance Service dataset. Methods: The study population consisted of patients with hip or knee OA aged ≥ 65 years.Psychotropic drugs included opioids, benzodiazepines, non-benzodiazepine hypnotics (Z-drugs), anti-epileptics, tricyclic antidepressants, selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), typical antipsychotics, atypical antipsychotics, and anxiolytics. The prevalence and long-term (> 3 months) prescription rates of psychotropic drugs in OA patients were calculated. Results: The study included 1,821,158 patients with OA (mean age 71.7 years; 65.32% female).Of the cohort, 49% had comorbidities for which psychotropics were indicated. The prevalence of psychotropic prescriptions decreased from 58.2% to 52.0% in 2018 and then leveled off.The long-term prescription rate remained constant until 2018 and then increased slightly.The most commonly prescribed psychotropics were opioids and long- and short-acting benzodiazepines. The prescription rates of opioids and long-acting benzodiazepines decreased from 2011 to 2020. For those with psychiatric co-morbidities, the prescription rates of anti-epileptics and SNRIs increased, while the prescription rates of anti-epileptics, SSRIs, other antidepressants, and atypical psychotropics increased for those without such co-morbidities. The most commonly prescribed psychotropics were diazepam and alprazolam, excluding tramadol and tramadol–acetaminophen combination. For those with psychiatric co-morbidities, the prescription rates of gabapentin and fentanyl increased, while for those without such co-morbidities, the prescription rates of lorazepam, fentanyl, escitalopram and quetiapine increased. Conclusion A significant number of older Korean patients with OA were prescribed psychotropic drugs in the absence of comorbidities requiring such drugs, including drugs that have little effect on OA and unfavorable safety profiles in older adults.

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: There are few safe effective ways to relieve osteoarthritis (OA) pain; as a result, off-label psychotropic drug prescriptions have increased worldwide. This study examined the change in psychotropic drug prescriptions for patients with OA from 2011 to 2020 using the Korean National Health Insurance Service dataset. Methods: The study population consisted of patients with hip or knee OA aged ≥ 65 years.Psychotropic drugs included opioids, benzodiazepines, non-benzodiazepine hypnotics (Z-drugs), anti-epileptics, tricyclic antidepressants, selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), typical antipsychotics, atypical antipsychotics, and anxiolytics. The prevalence and long-term (> 3 months) prescription rates of psychotropic drugs in OA patients were calculated. Results: The study included 1,821,158 patients with OA (mean age 71.7 years; 65.32% female).Of the cohort, 49% had comorbidities for which psychotropics were indicated. The prevalence of psychotropic prescriptions decreased from 58.2% to 52.0% in 2018 and then leveled off.The long-term prescription rate remained constant until 2018 and then increased slightly.The most commonly prescribed psychotropics were opioids and long- and short-acting benzodiazepines. The prescription rates of opioids and long-acting benzodiazepines decreased from 2011 to 2020. For those with psychiatric co-morbidities, the prescription rates of anti-epileptics and SNRIs increased, while the prescription rates of anti-epileptics, SSRIs, other antidepressants, and atypical psychotropics increased for those without such co-morbidities. The most commonly prescribed psychotropics were diazepam and alprazolam, excluding tramadol and tramadol–acetaminophen combination. For those with psychiatric co-morbidities, the prescription rates of gabapentin and fentanyl increased, while for those without such co-morbidities, the prescription rates of lorazepam, fentanyl, escitalopram and quetiapine increased. Conclusion A significant number of older Korean patients with OA were prescribed psychotropic drugs in the absence of comorbidities requiring such drugs, including drugs that have little effect on OA and unfavorable safety profiles in older adults.

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: In this study, we aimed to investigate whether opioid-sparing anesthesia (OSA) reduces postoperative nausea and vomiting (PONV) in patients undergoing laparoscopic gynecological surgery. Methods: Adult patients undergoing elective laparoscopic gynecological surgery were randomly assigned to either the opioid-using anesthesia (OUA) or the OSA groups. In the OUA group, remifentanil was administered as an opioid during general anesthesia. In the OSA group, apart from a single dose of 5 μg/kg of alfentanil for tracheal intubation, no other opioids were used. In both groups, a multimodal intravenous non-opioid analgesic regimen was used preferentially in the post-anesthesia care unit (PACU). The primary outcome was the incidence of PONV, assessed by symptoms until the postoperative day 1. Results: A total of 120 patients were included in this study. The incidence of nausea in the PACU was significantly lower in the OSA group compared to in the OUA group (31.7% in the OSA group vs. 51.7% in the OUA group, P = 0.026). Pain scores and the incidence of opioid analgesic administration were lower in the OSA group during PACU stay, resulting in a significantly lower number of patients requiring rescue opioid analgesics (3.3% vs. 18.3%, P = 0.008). There were no significant differences in intraoperative vital signs, hemodynamic interventions, or duration of PACU and hospital stay between the two groups. Conclusions OSA significantly reduced postoperative nausea, pain scores, and the need for rescue analgesics in the PACU without increasing hemodynamic instability in patients undergoing laparoscopic gynecological surgery.