Background: Drugs administered intravenously during the postoperative period can mix before entering the bloodstream. This study assessed the stability of mixtures of non-steroidal anti-inflammatory drugs (ketorolac and diclofenac) and antiemetics (ondansetron and ramosetron) to determine their suitability for concurrent administration. Methods: Ketorolac or diclofenac was combined with ondansetron or ramosetron at a 1:1 volume ratio. Each mixture was stored in a propylene syringe at 24°C for 2 hours. The mixtures were assessed visually, and the pH was measured. Additionally, the drug concentrations were determined using high-performance liquid chromatography (HPLC). Results: Mixtures of ketorolac or diclofenac and ramosetron showed no crystal formation or pH changes, and HPLC analysis confirmed that the drug concentrations remained stable. In contrast, mixtures of ketorolac or diclofenac and ondansetron exhibited the visible formation of 10–50 μm crystals under a microscope. However, there were no changes in the pH levels, and HPLC analysis indicated that the drug concentrations remained stable for both the mixtures. Conclusions Mixtures of ketorolac or diclofenac and ramosetron demonstrated physical and chemical stability for up to 2 hours, indicating that their concurrent use is feasible. Conversely, mixtures of ketorolac or diclofenac and ondansetron should be avoided due to the formation of crystals, even though the concentration of each drug remained stable.

Connective tissue disease (CTD), comprising a range of autoimmune disorders, is often accompanied by lung involvement, which can lead to life-threatening complications. The primary types of CTDs that manifest as interstitial lung disease (ILD) include rheumatoid arthritis, systemic sclerosis, Sjögren’s syndrome, mixed CTD, idiopathic inflammatory myopathies, and systemic lupus erythematosus. CTD-ILD presents a significant challenge in clinical diagnosis and management due to its heterogeneous nature and variable prognosis. Early diagnosis through clinical, serological, and radiographic assessments is crucial for distinguishing CTD-ILD from idiopathic forms and for implementing appropriate therapeutic strategies. Hence, we have reviewed the multiple clinical manifestations and diagnostic approaches for each type of CTD-ILD, acknowledging the diversity and complexity of the disease. The importance of a multidisciplinary approach in optimizing the management of CTD-ILD is emphasized by recent therapeutic advancements, which include immunosuppressive agents, antifibrotic therapies, and newer biological agents targeting specific pathways involved in the pathogenesis. Therapeutic strategies should be customized according to the type of CTD, the extent of lung involvement, and the presence of extrapulmonary manifestations. Additionally, we aimed to provide clinical guidance, including therapeutic recommendations, for the effective management of CTD-ILD, based on patient, intervention, comparison, outcome (PICO) analysis.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Background: Drugs administered intravenously during the postoperative period can mix before entering the bloodstream. This study assessed the stability of mixtures of non-steroidal anti-inflammatory drugs (ketorolac and diclofenac) and antiemetics (ondansetron and ramosetron) to determine their suitability for concurrent administration. Methods: Ketorolac or diclofenac was combined with ondansetron or ramosetron at a 1:1 volume ratio. Each mixture was stored in a propylene syringe at 24°C for 2 hours. The mixtures were assessed visually, and the pH was measured. Additionally, the drug concentrations were determined using high-performance liquid chromatography (HPLC). Results: Mixtures of ketorolac or diclofenac and ramosetron showed no crystal formation or pH changes, and HPLC analysis confirmed that the drug concentrations remained stable. In contrast, mixtures of ketorolac or diclofenac and ondansetron exhibited the visible formation of 10–50 μm crystals under a microscope. However, there were no changes in the pH levels, and HPLC analysis indicated that the drug concentrations remained stable for both the mixtures. Conclusions Mixtures of ketorolac or diclofenac and ramosetron demonstrated physical and chemical stability for up to 2 hours, indicating that their concurrent use is feasible. Conversely, mixtures of ketorolac or diclofenac and ondansetron should be avoided due to the formation of crystals, even though the concentration of each drug remained stable.

Connective tissue disease (CTD), comprising a range of autoimmune disorders, is often accompanied by lung involvement, which can lead to life-threatening complications. The primary types of CTDs that manifest as interstitial lung disease (ILD) include rheumatoid arthritis, systemic sclerosis, Sjögren’s syndrome, mixed CTD, idiopathic inflammatory myopathies, and systemic lupus erythematosus. CTD-ILD presents a significant challenge in clinical diagnosis and management due to its heterogeneous nature and variable prognosis. Early diagnosis through clinical, serological, and radiographic assessments is crucial for distinguishing CTD-ILD from idiopathic forms and for implementing appropriate therapeutic strategies. Hence, we have reviewed the multiple clinical manifestations and diagnostic approaches for each type of CTD-ILD, acknowledging the diversity and complexity of the disease. The importance of a multidisciplinary approach in optimizing the management of CTD-ILD is emphasized by recent therapeutic advancements, which include immunosuppressive agents, antifibrotic therapies, and newer biological agents targeting specific pathways involved in the pathogenesis. Therapeutic strategies should be customized according to the type of CTD, the extent of lung involvement, and the presence of extrapulmonary manifestations. Additionally, we aimed to provide clinical guidance, including therapeutic recommendations, for the effective management of CTD-ILD, based on patient, intervention, comparison, outcome (PICO) analysis.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Background: Drugs administered intravenously during the postoperative period can mix before entering the bloodstream. This study assessed the stability of mixtures of non-steroidal anti-inflammatory drugs (ketorolac and diclofenac) and antiemetics (ondansetron and ramosetron) to determine their suitability for concurrent administration. Methods: Ketorolac or diclofenac was combined with ondansetron or ramosetron at a 1:1 volume ratio. Each mixture was stored in a propylene syringe at 24°C for 2 hours. The mixtures were assessed visually, and the pH was measured. Additionally, the drug concentrations were determined using high-performance liquid chromatography (HPLC). Results: Mixtures of ketorolac or diclofenac and ramosetron showed no crystal formation or pH changes, and HPLC analysis confirmed that the drug concentrations remained stable. In contrast, mixtures of ketorolac or diclofenac and ondansetron exhibited the visible formation of 10–50 μm crystals under a microscope. However, there were no changes in the pH levels, and HPLC analysis indicated that the drug concentrations remained stable for both the mixtures. Conclusions Mixtures of ketorolac or diclofenac and ramosetron demonstrated physical and chemical stability for up to 2 hours, indicating that their concurrent use is feasible. Conversely, mixtures of ketorolac or diclofenac and ondansetron should be avoided due to the formation of crystals, even though the concentration of each drug remained stable.