RESUMEN
【Objective】 To investigate the factors influencing the length of hospital stays of the acute fatty liver of pregnancy (AFLP) patients, so as to establish the prediction model. 【Methods】 A total of 49 patients diagnosed as AFLP)in ShenZhen People’s Hospital between January 2008 and January 2023 were retrospectively analyzed. According to the median length of hospital stays, the patients were divided into two groups: Group A(n=21)and Group B(n=28). Preoperative general laboratory data, clinical features and postpartum adverse outcomes in both groups were analyzed. Multivariate binary logistic regression was used to analyze the independent factors affecting the length of hospital stays for AFLP, and a prediction model for hospitalization time was established. 【Results】 Comparison between Group B and Group A were as follows: hospital stays(d)(15.5 vs 8), preoperative icterus(%)[16(57.1%)vs 3(14.3%)], thrombin time(TT)(s)(24.2 vs 21.3), prothrombin time(PT)(s)(16.8 vs 15.3), activated partial thromboplastin time(APTT)(s)(52.3 vs 40.7), total bilirubin(TBIL)(μmol/L)(77.2 vs 45.2), indirect bilirubin(IBIL)(μmol/L)(21.2 vs 10), creatinine(Cre)(μmol/L)[(171.97±53.34) vs (131.81±45.06]), TT extension(%)[24(85.7%)vs 11(52.4%)], APTT extension(%)[27(96.4%)vs 7(33.3%)], IBIL elevation(%)[19(67.9%)vs 4(19%)], Cre concentration rise(%)[21(75%)vs 8(38%)], number of postpartum plasma exchange sessions(%)[23(82.1%)vs 5(23.8%)], postpregnancy co-infection phenomenon(%)[21(75%)vs 4(19%)], with Group B significantly higher than Group A. The preoperative platelet count(×109/L)(128 vs 221)and the concentration of fibrinogen(g/L)[0.9 vs 1.6] in Group B were significantly lower than those in Group A. Univariate logistic regression analysis showed that preoperative icterus, postpregnancy co-infection phenomenon, number of postpartum plasma exchange sessions, preoperative TT extension, preoperative APTT extension, Cre concentration rise were influencing factors for the hospital stays of AFLP patients. According to the minimum result of Akaike information criterion, the multivariate binary logistic regression analysis (step-wise selection) showed that the number of postpartum plasma exchange sessions, icterus, preoperative APTT extension were the independent risk factor influencing the hospital stays of AFLP patients, and the logistic regression prediction model was established by incorporating the above three factors. Regularization techniques were further employed in linear regression to address and assess overfitting issues. Additionally, the confidence interval for the estimated effect sizes in each model have been acquired by bootstrapping techniques. 【Conclusion】 Preoperative icterus, preoperative APTT extension(APTT>43s)and the number of postpartum plasma exchange sessions were the independent risk factor influencing the hospital stays of AFLP patients and the logistic regression prediction model with high predictive effectiveness was established successfully.
RESUMEN
【Objective】 To strengthen the management of transfusion adverse events, so as to reduce the occurrence of medical damage and accidents, and guarantee the safety of blood transfusion. 【Methods】 The adverse events of blood transfusion reported in our hospital from July 2016 to December 2022 were collected, the reasons were tracked, and continuous improvements were made. 【Results】 From 2016 to 2022, a total of 315 transfusion adverse events were reported, including 233(73.97%, 233/315) cases of transfusion reactions and 82(26.03%, 82/315) transfusion adverse events. There were 271 328 transfusion cases in the same period, and the incidence of transfusion reactions was 0.858 7‰(233/271 328). The number of transfusion application was 129 887, and the incidence of transfusion adverse event is 0.631 3‰(82/129 887). Sixty-eigtht(82.93%, 68/82) cases of transfusion adverse events were caused by human factors, while the other 14(17.07%, 14/82) cases were non-human factors. According to the linear regression analysis, we have concluded that the year is a significant indicator for transfusion reaction rates (P0.05). 【Conclusion】 Strengthening the management of reporting adverse events in clinical blood transfusion, monitoring the incidence, analyzing and improving different types of adverse events by management tools can reduce the medical risks of blood transfusion and help to guarantee the safety of clinical blood transfusion.