Non-steroidal anti-inflammatory drugs (NSAIDs) have been widely used in patients with respiratory infection, but their safety in coronavirus disease 19 (Covid-19) patients has not been fully investigated. We evaluated an association between NSAID use and outcomes of Covid-19. This study was a retrospective observational cohort study based on insurance benefit claims sent to the Health Insurance Review and Assessment Service of Korea by May 15, 2020. These claims comprised all Covid-19-tested cases and history of medical service use for the past 3 years in these patients. The primary outcome was all-cause mortality, and the secondary outcome was need for ventilator care. Among 7590 patients diagnosed with Covid-19, two distinct cohorts were generated based on NSAID or acetaminophen prescription within 2 weeks before Covid-19 diagnosis. A total of 398 patients was prescribed NSAIDs, and 2365 patients were prescribed acetaminophen. After propensity score matching, 397 pairs of data set were generated, and all-cause mortality of the NSAIDs group showed no significant difference compared with the acetaminophen group (4.0% vs. 3.0%; hazard ratio [HR], 1.33; 95% confidence interval [CI], 0.63–2.88; P = 0.46). The rate of ventilator care also did not show significantly different results between the two groups (2.0% vs. 1.3%; HR, 1.60; 95% CI 0.53–5.30; P = 0.42). Use of NSAIDs was not associated with mortality or ventilator care in Covid-19 patients. NSAIDs may be safely used to relieve symptoms in patients with suspicion of Covid-19.

This study aimed to investigate the association between glucose dysregulation and delirium after non-cardiac surgery. Among a total of 203,787 patients who underwent non-cardiac surgery between January 2011 and June 2019 at our institution, we selected 61,805 with available preoperative blood glucose levels within 24 h before surgery. Patients experiencing glucose dysregulation were divided into three groups: hyperglycemia, hypoglycemia, and both. We compared the incidence of postoperative delirium within 30 days after surgery between exposed and unexposed patients according to the type of glucose dysregulation. The overall incidence of hyperglycemia, hypoglycemia, and both was 5851 (9.5%), 1452 (2.3%), and 145 (0.2%), respectively. The rate of delirium per 100 person-months of the exposed group was higher than that of the unexposed group in all types of glucose dysregulation. After adjustment, the hazard ratios of glucose dysregulation in the development of delirium were 1.35 (95% CI, 1.18–1.56) in hyperglycemia, 1.36 (95% CI, 1.06–1.75) in hypoglycemia, and 3.14 (95% CI, 1.27–7.77) in both. The subgroup analysis showed that exposure to hypoglycemia or both to hypo- and hyperglycemia was not associated with delirium in diabetic patients, but hyperglycemia was consistently associated with postoperative delirium regardless of the presence of diabetes. Preoperative glucose dysregulation was associated with increased risk of delirium after non-cardiac surgery. Our findings may be helpful for preventing postoperative delirium, and further investigations are required to verify the association and mechanisms for the effect we observed.

Background: Machine-learning techniques are useful for creating prediction models in clinical practice. This study aimed to construct a prediction model of postoperative 30-day mortality based on an automatically extracted electronic preoperative evaluation sheet. Methods: We used data from 276,341 consecutive adult patients who underwent non-cardiac surgery between January 2011 and December 2020 at a tertiary center for model development and internal validation, and another dataset from 63,384 patients between January 2011 and October 2021 at another center for external validation. Postoperative 30-day mortality was 0.16%. We developed an extreme gradient boosting (XGB) prediction model using only variables from preoperative evaluation sheets. Results: The model yielded an area under the curve of 0.960 and an area under the precision and recall curve of 0.216, which were 0.932 and 0.122, respectively, in the external validation set. The optimal threshold calculated by Youden’s J statistic had a sensitivity of 0.885 and specificity of 0.914. In an additional analysis with balanced distribution, the model showed a similar predictive value. Conclusion: We presented a machine-learning prediction model for 30-day mortality after non-cardiac surgery using preoperative variables automatically extracted from electronic medical records and validated the model in a multi-center setting. Our model may help clinicians predict postoperative outcomes.

Studies have reported conflicting results on the association between the use of renin-angiotensin-aldosterone system (RAAS) inhibitors and cancer development. We compared the incidence of cancer between patients using RAAS inhibitors and other antihypertensive drugs. This retrospective observational cohort study used data from seven hospitals in Korea that were converted for use in the Observational Medical Outcomes Partnership Common Data Model. A total of 166,071 patients on antihypertensive therapy across the databases of the seven hospitals were divided into two groups according to the use of RAAS inhibitors. The primary outcome was the occurrence of cancer. A total of 166,071 patients across the databases of the seven hospitals was included in the final analysis; 26,650 (16%) were in the RAAS inhibitors group and 139,421 (84%) in the other antihypertensive drugs group. The meta-analysis of the whole cohort showed a lower incidence of cancer occurrence in the RAAS inhibitor group (9.90 vs. 13.28 per 1000 person years; HR, 0.81; 95% confidence interval [CI], 0.75–0.88). After propensity-score matching, the RAAS inhibitor group consistently showed a lower incidence of cancer (9.90 vs. 13.28 per 1000 person years; HR, 0.86; 95% CI, 0.81–0.91). The patients using RAAS inhibitors showed a lower incidence of cancer compared with those using other antihypertensive drugs. These findings support the association between the use of RAAS inhibitors and cancer occurrence.

Background: The effect of renin-angiotensin-aldosterone system (RAAS) inhibitors in coronavirus disease 19 (Covid-19) patients has not been fully investigated. We evaluated the association between RAAS inhibitor use and outcomes of Covid-19. Methods: This study was a retrospective observational cohort study that used data based on insurance benefit claims sent to the Health Insurance Review and Assessment Service of Korea by May 15, 2020. These claims comprised all Covid-19 tested cases and the history of medical service use in these patients for the past five years. The primary outcome was all-cause mortality, and the rate of ventilator care was compared between the groups. Results: From a total of 7,590 patients diagnosed with Covid-19, two distinct cohorts were generated based on RAAS inhibitors prescribed within 6 months before Covid-19 diagnosis. A total of 1,111 patients was prescribed RAAS inhibitors, and 794 patients were prescribed antihypertensive drugs, excluding RAAS inhibitors. In propensity-score matched analysis, 666 pairs of data set were generated, and all-cause mortality of the RAAS inhibitor group showed no significant difference compared with the non-RAAS inhibitor group (14.6% vs. 11.1%; hazard ratio [HR], 0.79; 95% confidence interval [CI], 0.54–1.15; p = 0.22). The rate of ventilator care was not significantly different between the two groups (4.4% vs. 4.1%; HR, 1.04; 95%CI, 0.60–1.79; p = 0.89). Conclusions: RAAS inhibitor treatment did not appear to increase the mortality of Covid-19 patients compared with other antihypertensive drugs, suggesting that they may be safely continued in Covid-19 patients.

Contradictory findings exist about association of angiotensin-converting enzyme inhibitor (ACEi) and angiotensin receptor blocker (ARB) with lung cancer development. This was a retrospective observational cohort study that used data from 7 hospitals in Korea, converted to the Observational Medical Outcomes Partnership Common Data Model. The primary outcome was occurrence of lung cancer. A total of 207,794 patients across the 7 databases was included in the final analysis; 33,230 (16%) were prescribed ACEi and 174,564 (84%) were prescribed ARB. Crude analysis adjusted for sex and age showed higher incidence of lung cancer in the ACEi group compared to the ARB group (hazard ratio [HR], 1.46; 95% confidence rate [CI], 1.08–1.97). After propensity-score matching, 30,445 pairs were generated, and there was no difference in incidence of lung cancer between the two groups (HR, 0.93; 95% CI, 0.64–1.35). Patients prescribed ACEi showed no difference in incidence of lung cancer development compared to those using ARB. This finding provides evidence on the association between ACEi and occurrence of lung cancer.