Objective:To screen the independent influencing factors of restoration of spontaneous circulation (ROSC) in patients after cardiopulmonary resuscitation (CPR) and establish a predictive model, and explore its clinical value.Methods:A retrospective case control study was conducted. The clinical data of cardiac arrest patients admitted to the emergency department of Tangdu Hospital of Air Force Military Medical University and received CPR from January to July 2023 were analyzed, including general information, blood biochemical indicators, main cause of cardiac arrest, whether it was defibrillation rhythm, duration from admission to CPR, and whether ROSC was achieved. The clinical data between the patients whether achieved ROSC or not were compared. The binary multivariate Logistic regression analysis was used to screen the independent influencing factors of ROSC in in-hospital CPR patients. According to the above influencing factors, the ROSC prediction model was established, and the receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of the model for ROSC.Results:A total of 235 patients who received CPR in the emergency department were enrolled, including 153 cases (65.11%) of in-hospital CPR and 82 cases (34.89%) of out-of-hospital CPR. The ROSC ratio was 30.21% (71/235). Among all patients, the majority were aged 61-80 years [40.43% (95/235)], and cardiogenic disease was the main cause of cardiac arrest [32.77% (77/235)]. Among 153 patients with in-hospital CPR, 89 were non-ROSC and 64 were ROSC with ROSC rate of 41.83%. Compared with the non-ROSC group, the patients in the ROSC group had lower blood lactic acid (Lac), N-terminal pro-brain natriuretic peptide (NT-proBNP), Lac/albumin (Alb) ratio (LAR), and ratio of non-defibrillation rhythm [Lac (mmol/L): 5.50 (2.33, 9.65) vs. 7.10 (3.50, 13.35), NT-proBNP (μg/L): 0.87 (0.20, 8.68) vs. 3.00 (0.58, 20.17), LAR: 0.14 (0.07, 0.29) vs. 0.19 (0.10, 0.43), non-defibrillation rhythm ratio: 68.75% (44/64) vs. 93.26% (83/89)], higher actual base excess (ABE) and Alb [ABE (mmol/L): -3.95 (-12.75, 0.23) vs. -7.50 (-13.50, -3.35), Alb (g/L): 38.13±7.03 vs. 34.09±7.81], and shorter duration from admission to CPR [hours: 3.25 (1.00, 14.00) vs. 8.00 (2.00, 27.50)], the differences were statistically significant (all P < 0.05). Binary multivariate Logistic regression analysis showed that LAR [odds ratio ( OR) = 0.037, 95% confidence interval (95% CI) was 0.005-0.287], non-defibrillation rhythm ( OR = 0.145, 95% CI was 0.049-0.426), and duration from admission to CPR ( OR = 0.984, 95% CI was 0.972-0.997) were independent influencing factors for ROSC in hospitalized CPR patients (all P < 0.05). Based on the above influencing factors, a ROSC prediction model was constructed through regression analysis results. The ROC curve analysis showed that the area under the ROC curve (AUC) for predicting ROSC in in-hospital CPR patients was 0.757 (95% CI was 0.680-0.834), Yoden index was 0.429, sensitivity was 76.6%, and specificity was 66.3%. Conclusions:LAR, non-defibrillation rhythm and duration from admission to CPR were independent influencing factors for ROSC in patients with in-hospital CPR. The ROSC prediction model established based on the above influencing factors has a good predictive value for ROSC of CPR patients in hospital, and can guide clinicians to evaluate the prognosis of patients through relevant indicators as early as possible.

Objective:To analyze the early warning value of laboratory examination on admission of patients with hemorrhagic fever with renal syndrome to critically ill patients.Meetods:In this study, a retrospective case-control study was used to analyze the clinical data and laboratory examination results of patients with hemorrhagic fever with renal syndrome admitted to the emergency department of Tangdu Hospital of Air Force Medical University from January 2021 to January 2022. According to the patient's laboratory indexes and clinical symptoms, the patients were divided into mild, moderate, severe and critical groups. The general data of the two groups were compared, and the independent risk factors of critically ill patients were screened by multi-factor logistic regression analysis, the predictive model of severe HFRS patients was constructed, and the ROC curve was drawn. .Results:Of the 164 patients with HFRS, 50 were in the severe group and 114 in the mild group. The serum levels of WBC, AST, ALT, Cr, BUN, DD and PCT in the severe group were higher than those in the mild group, while the levels of PLT, ALB and PTA in the severe group were lower than those in the mild group. Multiple logistic regression analysis showed that WBC, PLT and PCT were independent influencing factors for the progression of critically ill patients. The predictive model of severe HFRS was established as follows: logit (P) = -0.321 + 0.040 WBC (×10 9/L) -0.045 PLT (×10 9/L) + 0.086 PCT(ng/mL). The early warning ef?cacy of WBC, PLT, And PCT for severe HFRS was further analyzed. The area under the ROC curve (area under curve, AUC) was 0.779, 0.842, 0.862, and the optimal threshold was 10.435×109/L, 41.5 ×109/Land 2.97 ng/mL, respectively. The AUC of joint detection is 0.900, the sensitivity is 88.0%, and the speci?city is 82.5%, which is better than that of a single laboratory. . Conclusions:HFRS laboratory indexes have certain clinical signi?cance for the identi?cation of critically ill patients, in which serum WBC, PLT and PCT indexes are the risk factors of severe HFRS, which provides a theoretical basis for clinical diagnosis, treatment and prognosis of severe HFRS patients.