秦皇岛地区基于Utstein模式溺水数据库的建立及相关因素分析

Establishment of a drowning database based on the utstein model and analysis of related factors in the Qinhuangdao region

摘要:

目的 通过建立基于心肺复苏乌斯坦因（Utstein）评估模式的溺水数据库，分析秦皇岛地区溺水者的临床特征及影响溺水者存活的关键因素，为溺水急救和治疗提供更有针对性的策略和方法。方法 选取2019年6月—2022年11月秦皇岛地区医院收治的108例溺水者，收集溺水者的一般临床资料，包括性别、年龄、溺水类型、既往病史、溺水者初始情况、急救人员是否心肺复苏、首次急救医疗服务（EMS）时间、首次心肺复苏（CPR）时间、现场护理结束（EOSC）时间、意识清醒时间、到达医院是否正在心肺复苏、心肺复苏持续时间、院内心肺复苏类型、生命体征（体温、收缩压、舒张压、心率、呼吸、外周氧饱和度）、院内首次监护心率（室颤、室速、无脉搏新店活动）、初次院内神经系统检查［格拉斯哥昏迷评分（GCS），清醒、言语反应、疼痛反应、无反应评分（AVPU）］、血气［氧分压、二氧化碳分压、pH值、呼气末二氧化碳］、血乳酸水平、气道或通气（无、普通氧气、无创通气、有创通气、非常规有创通气）、肺水肿、自主循环恢复（ROSC）后96 h体温（T_max、T_min）、自主循环恢复（ROSC）后24 h血糖、血糖能否维持、低血压发作（收缩压≤90 mmHg）、持续升压药/正性肌力药支持、溺水并发症（急性呼吸窘迫综合征、弥散性血管内凝血、肺炎、胰腺炎、急性肾损伤、休克、多系统器官衰竭、败血症、电解质紊乱、葡萄糖紊乱）、出院后去向（其他医院、回家、康复疗养院、其他）、出院时情况（脑功能分级、GCS评分、AVPU评分、有无自主呼吸）、院内死亡原因（呼吸窘迫综合征、弥散性血管内凝血、颅内高压、电解质紊乱、急性肾衰竭、癫痫发作、败血症或心肌衰竭）。采用多因素逐步Logistic回归模型分析溺水者存活的危险因素。结果 存活组与死亡组的年龄、是否存在既往病史、有无目击者、有无脉搏/心跳、有无呼吸、首次EMS时间、首次CPR时间、EOSC时间、血乳酸水平、有无肺水肿、ROSC后96 h体温T_max、ROSC后96 h体温T_min、溺水并发急性呼吸窘迫综合征、弥散性血管内凝血、急性肾损伤、休克、多系统器官衰竭、败血症、电解质紊比较，差异均有统计学意义（P <0.05）。多因素逐步Logistic回归分析结果显示，年龄、既往病史、目击者、脉搏/心跳、有无呼吸、首次EMS时间、首次CPR时间、EOSC时间、血乳酸水平、肺水肿、ROSC后96 h体温T_max、ROSC后96 h体温T_min、溺水并发症均是溺水者存活的影响因素（P <0.05）。基于以上指标构建的列线图预测模型分析显示，其具有较好的区分能力；Bootstrap验证也表明，模型的偏差校准曲线与理想曲线吻合良好。结论 本研究基于Utstein评估模式建立的溺水数据库有效地揭示了影响溺水者存活的多种危险因素。这些发现对于提高溺水者的存活率和改善治疗效果具有重要意义。未来的急救和治疗中，需加强对关键影响因素的关注和干预并掌握心肺复苏的重要性。

Abstract:

Objective To analyze the clinical characteristics of drowning patients in Qinhuangdao area and the key factors affecting survival by establishing a drowning database based on the Utstein model, and to provide more targeted strategies and methods for drowning rescue and treatment.Methods A total of 108 drowning patients treated at hospitals in Qinhuangdao area from June 2019 to November 2022 were selected. General clinical data of patients were collected, including gender, age, drowning type, medical history, initial condition of drowning, whether cardiopulmonary resuscitation (CPR) was performed by first-aid personnel, emergency medical services (EMS) time, first CPR time, end of on-site care (EOSC) time, time to consciousness recovery, whether CPR was in progress upon arrival at the hospital, duration of CPR, in-hospital CPR type, vital signs (temperature, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, peripheral oxygen saturation), initial monitoring heart rate after ROSC (ventricular fibrillation, ventricular tachycardia, pulseless electrical activity), initial in-hospital neurological examination [Glasgow Coma Scale (GCS), alertness, verbal response, painful response, AVPU score], blood gases (partial pressure of oxygen, partial pressure of carbon dioxide, pH value, end-tidal carbon dioxide), blood lactate level, airway or ventilation (none, regular O₂, non-invasive ventilation, invasive ventilation, unconventional invasive ventilation), pulmonary edema, initial 96 h body temperature after ROSC (T_max, T_min), 24 h blood sugar after ROSC, ability to maintain blood sugar, occurrence of hypotension (systolic blood pressure ≤90 mmHg), continuous use of vasopressors/positive inotropic agents, complications of drowning (acute respiratory distress syndrome, disseminated intravascular coagulation, pneumonia, pancreatitis, acute kidney injury, shock, multiple organ failure, sepsis, electrolyte imbalance, glucose disorder), discharge destination (other hospital, home, rehabilitation nursing home, other), condition at discharge (cerebral function grading, GCS score, AVPU score, presence of spontaneous respiration), cause of in-hospital death (acute respiratory distress syndrome, disseminated intravascular coagulation, intracranial hypertension, electrolyte imbalance, acute renal failure, seizure, sepsis or heart failure). Multivariate stepwise logistic regression analysis was used to analyze the risk factors for survival of drowning patients.Results There were statistically significant differences in age, presence of previous medical history, presence of witnesses, presence of pulse/heart rate, presence of breathing, EMS time, first CPR time, EOSC time, blood lactate level, presence of pulmonary edema, initial 96 h body temperature after ROSC (T_max, T_min), complications of drowning (acute respiratory distress syndrome, disseminated intravascular coagulation, acute kidney injury, shock, multiple organ failure, sepsis, electrolyte imbalance) between the two groups (P < 0.05). Multivariate stepwise logistic regression analysis showed that age, medical history, witnesses, presence of pulse/heart rate, presence of breathing, EMS time, first CPR time, EOSC time, blood lactate level, presence of pulmonary edema, initial 96 h body temperature T_max after ROSC, initial 96 h body temperature T_min after ROSC, complications of drowning were all factors affecting the clinical outcome of drowning patients (P < 0.05). The predictive model analysis based on the above indicators showed that the model had good discriminative ability; Bootstrap validation showed that the deviation calibration curve of the model was well matched with the ideal curve.Conclusion The drowning database established based on the Utstein model effectively revealed various risk factors affecting the survival of drowning patients. These findings are of great significance for improving the survival rate of drowning patients and improving treatment outcomes. It is recommended to strengthen the attention and intervention to key influencing factors in future first aid and treatment, and to timely perform cardiopulmonary resuscitation.