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Epilepsy is a common disease in nervous system, of which patients often present with spontaneous unpredictable spontaneous seizures. Sudden unexpected death in epilepsy (SUDEP) is one of the most serious complications of epilepsy, and it is also the main cause of premature death of epileptic patients. Generalized tonic-clonic seizures, age and genetic factors are common risk factors of SUDEP. This article summarizes the classification of SUDEP and epidemiology, mechanism, risk factors, risk assessment and preventive methods of SUDEP to help physicians to understand the difference between SUDEP and sudden cardiac death.
ABSTRACT
Objective:To investigate the safety and efficacy of individualized sunitinib schedule for patients with metastatic renal cell carcinoma (mRCC) according to the monitoring results of plasma drug concentration.Methods:The clinical data of patients with mRCC who received sunitinib treatment in our center from January 2014 to December 2020 were retrospectively analyzed, including 20 patients who underwent monitoring of plasma drug concentration (monitoring group), and 45 patients, matched by propensity score matching, received sunitinib but did not undergo monitoring of plasma drug concentration during the same period (unmonitored group). In the monitoring group, there were 12 males and 8 females. The mean age was 52.9 years, and ECOG score ≤1 in 16 cases (80%). Three patients were in the IMDC favorable-risk group, 15 patients were in the intermediate-risk group, and 2 patients were in the high-risk group. There were 18 cases of clear cell carcinoma and 2 cases of non-clear cell carcinoma, 5 cases of ISUP grade 1-2 and 11 cases of grade 3-4. In the unmonitored group, there were 31 males and 14 females. The mean age was 57.7 years, and 30 patients had ECOG score ≤1, 15 cases ≥2. There were 10 cases in IMDC favorable-risk group, 23 cases in intermediate-risk group, and 12 cases in high-risk group. Thirty-seven cases were clear cell carcinoma and 8 cases were non-clear cell carcinoma, 8 cases were in ISUP grade 1-2 and 28 cases in grade 3-4. There were no statistically significant differences between the two groups in the above parameters ( P>0.05). The monitoring group used the regimen of taking sunitinib for 4 weeks and stopping for 2 weeks (4/2 week) in the first cycle. The blood concentration of sunitinib was monitored before the first cycle and on days 4, 7, 10, 14, 21 and 28, and personalized medication plan was formulated according to the curve of the blood concentration. The 4/2 week scheme was adopted in the undetected monitoring group.The two groups were compared in the incidence of adverse events (AEs), progression-free survival (PFS), overall survival (OS), tumor treatment response and other clinical outcomes. Results:In the monitoring group, 90% (18/20) of patients receiving sunitinib had a steady-state plasma concentration of more than 150ng/ml, of which 10 patients (50%) had a plasma concentration of 150-200 ng/ml and 8 patients (40%) had a plasma concentration of more than 200 ng/ml. Meanwhile, all patients with plasma concentration higher than 150 ng/ml developed severe AEs (grade 3 and above) after treatment. The other two patients' plasma concentration were 100-150 ng/ml, and did not have severe AEs.All patients in the monitoring group received individualized medication schedule adjustment according to the plasma drug concentration and the occurrence point of severe AEs, ensuring that the peak plasma drug concentration was maintained at about 100-150 ng/ml. Among them, 6 patients were changed to take 2 weeks and stop for 1 week (2/1 week schedule), 4 patients were changed to take 10 days and stop for 5 days (10/5 d schedule), 7 patients were changed to take 7 days and stop for 3 days (7/3 d schedule), and 3 patients were changed to take 5 days and stop for 2 days (5/2 d schedule). The incidence of severe AEs significantly decreased from 90% (18/20) to 35% (7/20), and the difference was statistically significant ( P=0.003), while the incidence of grade 3 and higher AEs was 55.6% (25/45) in the standard group, which was statistically significant compared with the incidence of severe AEs before adjustment in the monitoring group ( P=0.006). Further analysis of the efficacy difference between the two groups showed that the overall objective response rate in the monitoring group (40%, 8/20) was higher than that in the standard group (20%, 9/45), although the difference was not statistically significant ( P=0.09). Median PFS and OS were significantly longer in the monitored group than in the standard group (PFS: 23 vs. 10 months, P=0.002; OS: not reached vs.25 months, P=0.005). Conclusions:The bioavailability of sunitinib is high in mRCC patients, which may lead to higher plasma drug concentration, adjustment of medication regimen based on blood concentration monitoring significantly improved patient safety and clinical outcomes. However, further validation by larger-scale, multi-center and prospective studies is needed.
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Objective:To establish an outpatient operation simulation model by using pediatric outpatient data of a third-level maternal and child health hospital in Shenzhen, then to explore and improve hospital management problems.Methods:The hospital information system was used to extract the parameters of the pediatric outpatient data, and the AnyLogic simulation model was used to compare the hospital operation data and simulation data. Through the ideal operation status of the model and the actual state data of the hospital, the problems that were easily overlooked and difficult to investigate were identified. Then the problems were confirmed through on-site tracking investigations, improve process execution, and reduce waiting and congestion.Results:The gap between simulation data and hospital operation data was reflected in the waiting links of the first visit, inspection project and report interpretation. The process transformation of these three links could be the key to improving the overall service capability.Conclusions:Simulation and field tracking are effective ways to explore process problems. The method that simulation model is used to realize the method path of "big data→simulation→site tracking→management analysis improvement" , is suitable for the application and research of hospital process problem discovery.