Prehospital identification of futile resuscitation efforts (defined as a predicted probability of survival lower than 1%) for out-of-hospital cardiac arrest (OHCA) may reduce unnecessary transport. Reliable prediction variables for OHCA 'termination of resuscitation' (TOR) rules are needed to guide treatment decisions. The Universal TOR rule uses only three variables (Absence of Prehospital ROSC, Event not witnessed by EMS and no shock delivered on the scene) has been externally validated and is used by many EMS systems. Deep learning, an artificial intelligence (AI) platform is an attractive model to guide the development of TOR rule for OHCA. The purpose of this study was to assess the feasibility of developing an AI-TOR rule for neurologically favorable outcomes using general purpose AI and compare its performance to the Universal TOR rule. Methods: We identified OHCA cases of presumed cardiac etiology who were 18 years of age or older from 2016 to 2019 in the All-Japan Utstein Registry. We divided the dataset into 2 parts, the first half (2016–2017) was used as a training dataset for rule development and second half (2018–2019) for validation. The AI software (Prediction One®) created the model using the training dataset with internal cross-validation. It also evaluated the prediction accuracy and displayed the ranking of influencing variables. We performed validation using the second half cases and calculated the prediction model AUC. The top four of the 11 variables identified in the model were then selected as prognostic factors to be used in an AI-TOR rule, and sensitivity, specificity, positive predictive value, and negative predictive value were calculated from validation cohort. This was then compared to the performance of the Universal TOR rule using same dataset. There were 504,561 OHCA cases, 18 years of age or older, 302,799 cases were presumed cardiac origin. Of these, 149,425 cases were used for the training dataset and 153,374 cases for the validation dataset. The model developed by AI using 11 variables had an AUC of 0.969, and its AUC for the validation dataset was 0.965. The top four influencing variables for neurologically favorable outcome were Prehospital ROSC, witnessed by EMS, Age (68 years old and younger) and nonasystole. The AUC calculated using the 4 variables for the AI-TOR rule was 0.953, and its AUC for the validation dataset was 0.952 (95%CI 0.949 –0.954). Of 80,198 patients in the validation cohort that satisfied all four criteria for the AI-TOR rule, 58 (0.07%) had a neurologically favorable one-month survival. The specificity of AI-TOR rule was 0.990, and the PPV was 0.999 for predicting lack of neurologically favorable survival, both the specificity and PPV were higher than that achieved with the universal TOR (0.959, 0.998). The accuracy of prediction models using AI software to determine outcomes in OHCA was excellent and the AI-TOR rule’s variables from prediction model performed better than the Universal TOR rule. External validation of our findings as well as further research into the utility of using AI platforms for TOR prediction in clinical practice is needed.

中文翻译：

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使用通用人工智能 (AI) 制定和验证院外心脏骤停 (OHCA) 病例的院前复苏终止 (TOR) 规则

院前识别院外心脏骤停 (OHCA) 的无效复苏努力（定义为预测生存概率低于 1%）可能会减少不必要的转运。 OHCA“复苏终止”(TOR) 规则需要可靠的预测变量来指导治疗决策。通用 TOR 规则仅使用三个变量（没有院前 ROSC、EMS 未目睹的事件以及现场未进行电击）已经过外部验证并被许多 EMS 系统使用。深度学习这个人工智能 (AI) 平台是指导 OHCA TOR 规则开发的一个有吸引力的模型。本研究的目的是评估使用通用人工智能开发 AI-TOR 规则以获得神经学有利结果的可行性，并将其性能与通用 TOR 规则进行比较。方法：我们在全日本 Utstein 登记处确定了 2016 年至 2019 年间年龄在 18 岁或以上、推测为心脏病原因的 OHCA 病例。我们将数据集分为两部分，前半部分（2016-2017）用作规则开发的训练数据集，后半部分（2018-2019）用于验证。 AI 软件 (Prediction One®) 使用训练数据集和内部交叉验证创建模型。它还评估了预测准确性并显示了影响变量的排名。我们使用后半个案例进行验证并计算预测模型 AUC。然后选择模型中确定的 11 个变量中的前 4 个作为 AI-TOR 规则中使用的预后因素，并根据验证队列计算敏感性、特异性、阳性预测值和阴性预测值。然后将其与使用相同数据集的通用 TOR 规则的性能进行比较。共有 504,561 例 OHCA 病例，年龄在 18 岁或以上，其中 302,799 例被推测为心源性。其中，149,425 个案例用于训练数据集，153,374 个案例用于验证数据集。 AI 使用 11 个变量开发的模型的 AUC 为 0.969，其验证数据集的 AUC 为 0.965。神经学良好结果的前四个影响变量是院前 ROSC（由 EMS 见证）、年龄（68 岁及以下）和非心搏停止。使用 AI-TOR 规则的 4 个变量计算的 AUC 为 0.953，验证数据集的 AUC 为 0.952 (95%CI 0.949 –0.954)。在验证队列中满足 AI-TOR 规则所有四个标准的 80,198 名患者中，58 名 (0.07%) 具有神经学上有利的一个月生存率。 AI-TOR规则的特异性为0.990，预测缺乏神经学有利生存的PPV为0.999，特异性和PPV均高于通用TOR所实现的结果（0.959，0.998）。使用 AI 软件确定 OHCA 结果的预测模型的准确性非常好，并且来自预测模型的 AI-TOR 规则变量的表现优于通用 TOR 规则。需要对我们的研究结果进行外部验证，并进一步研究在临床实践中使用人工智能平台进行 TOR 预测的效用。