Ericson Oskar, Hjelmgren Jonas, Sjövall Fredrik, Söderberg Joakim, Persson Inger
The Swedish Institute for Health Economics (IHE), Lund, Sweden.
Department of Intensive and Perioperative Medicine, Skåne University Hospital, Malmö, Sweden.
J Health Econ Outcomes Res. 2022 Apr 26;9(1):101-110. doi: 10.36469/jheor.2022.33951. eCollection 2022.
Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, and reduce mortality. The sepsis machine learning algorithm NAVOY® Sepsis, based on variables routinely collected at intensive care units (ICUs), has shown excellent predictive properties. However, the economic consequences of forecasting the onset of sepsis are unknown. The potential cost and cost-effectiveness impact of a machine learning algorithm forecasting the onset of sepsis was estimated in an ICU setting. A health economic model has been developed to capture short-term and long-term consequences of sepsis. The model is based on findings from a randomized, prospective clinical evaluation of NAVOY® Sepsis and from literature sources. Modeling the relationship between time from sepsis onset to treatment and prevalence of septic shock and in-hospital mortality were of particular interest. The model base case assumes that the time to treatment coincides with the time to detection and that the algorithm predicts sepsis 3 hours prior to onset. Total costs include the costs of the prediction algorithm, days spent at the ICU and hospital ward, and long-term consequences. Costs are estimated for an average patient admitted to the ICU and for the healthcare system. The reference method is sepsis diagnosis in accordance with clinical practice. In Sweden, the total cost per patient amounts to €16 436 and €16 512 for the algorithm and current practice arms, respectively, implying a potential cost saving per patient of €76. The largest cost saving is for the ICU stay, which is reduced by 0.16 days per patient (5860 ICU days for the healthcare sector) resulting in a cost saving of €1009 per ICU patient. Stochastic scenario analysis showed that NAVOY® Sepsis was a dominant treatment option in most scenarios and well below an established threshold of €20 000 per quality-adjusted life-year. A 3-hour faster detection implies a reduction in in-hospital mortality, resulting in 356 lives saved per year. A sepsis prediction algorithm such as NAVOY® Sepsis reduces the cost per ICU patient and will potentially have a substantial cost-saving and life-saving impact for ICU departments and the healthcare system.
脓毒症的早期诊断已被证明可减少治疗延误、增加适当护理并降低死亡率。基于重症监护病房（ICU）常规收集变量的脓毒症机器学习算法NAVOY® Sepsis已显示出出色的预测性能。然而，预测脓毒症发作的经济后果尚不清楚。在ICU环境中估计了预测脓毒症发作的机器学习算法的潜在成本和成本效益影响。已开发出一种健康经济模型来捕捉脓毒症的短期和长期后果。该模型基于NAVOY® Sepsis的随机前瞻性临床评估结果和文献资料。对从脓毒症发作到治疗的时间与感染性休克患病率及住院死亡率之间的关系进行建模尤为重要。模型基本情况假设治疗时间与检测时间一致，且该算法在发作前3小时预测脓毒症。总成本包括预测算法成本、在ICU和医院病房的住院天数以及长期后果。针对入住ICU的平均患者和医疗保健系统估算成本。参考方法是按照临床实践进行脓毒症诊断。在瑞典，算法组和当前实践组每位患者的总成本分别为16436欧元和16512欧元，这意味着每位患者潜在节省成本76欧元。最大的成本节省在于ICU住院时间，每位患者减少0.16天（医疗保健部门减少5860个ICU住院日），导致每位ICU患者节省成本1009欧元。随机情景分析表明，在大多数情景中，NAVOY® Sepsis是占主导地位的治疗选择，且远低于每质量调整生命年20000欧元的既定阈值。提前3小时检测意味着住院死亡率降低，每年可挽救356条生命。像NAVOY® Sepsis这样的脓毒症预测算法可降低每位ICU患者的成本，并可能对ICU科室和医疗保健系统产生重大的成本节省和挽救生命的影响。
