正在针对 COVID-19 研究或开发各种预后和临床风险评分;但是,需要先在各类人群中进行进一步外部验证,然后才可推荐使用。世界卫生组织建议使用临床判断(包括考虑患者价值观和偏好,以及在可行情况下考虑当地和国家政策),而非当前可行的预后预测模型,进行管理决策指导。[2]World Health Organization. COVID-19 clinical management: living guidance. 2021 [internet publication].
https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2021-1
A-DROP:CURB-65 的改良版,与其他广泛使用的社区获得性肺炎评分相比,入院时预测 COVID-19 肺炎患者院内死亡的准确性更高。[1082]Fan G, Tu C, Zhou F, et al. Comparison of severity scores for COVID-19 patients with pneumonia: a retrospective study. Eur Respir J. 2020 Jul 16 [Epub ahead of print].
https://erj.ersjournals.com/content/early/2020/07/06/13993003.02113-2020
http://www.ncbi.nlm.nih.gov/pubmed/32675205?tool=bestpractice.com
APACHE II:一种可预测 COVID-19 患者住院死亡率的有效临床工具,其效能优于 SOFA 和 CURB-65 评分。得分达 17 及以上是死亡的早期标志,可能有助于提供指导,以做出进一步临床决策。[1083]Zou X, Li S, Fang M, et al. Acute physiology and chronic health evaluation II score as a predictor of hospital mortality in patients of coronavirus disease 2019. Crit Care Med. 2020 May 1;48(8):e657-65.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217128/
http://www.ncbi.nlm.nih.gov/pubmed/32371611?tool=bestpractice.com
CALL:一种基于以下四个因素对患者进行评分的危险因素评分系统:合并症、年龄、淋巴细胞计数和乳酸脱氢酶水平。一项研究发现,96% 的低 CALL 评分患者并未进展至重症。[1084]Ji D, Zhang D, Xu J, et al. Prediction for progression risk in patients with COVID-19 pneumonia: the CALL score. Clin Infect Dis. 2020 Apr 9 [Epub ahead of print].
https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa414/5818317
http://www.ncbi.nlm.nih.gov/pubmed/32271369?tool=bestpractice.com
COVID-GRAM:一种基于网络的计算器,用于估计患者进展至危重症的概率,基于以下入院时的 10 个变量:胸部放射影像学检查异常、年龄、咯血、呼吸困难、无意识、合并症数量、癌症史、中性粒细胞-淋巴细胞比值、乳酸脱氢酶水平和直接胆红素水平。需开展更多验证性研究,尤其是在中国之外。[1085]Liang W, Liang H, Ou L, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med. 2020 May 12 [Epub ahead of print].
https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2766086
http://www.ncbi.nlm.nih.gov/pubmed/32396163?tool=bestpractice.com
COVID-19MRS:一种快速、独立于操作者的临床工具,在一项回顾性队列研究中发现该工具可以客观预测死亡率。[1086]Fumagalli C, Rozzini R, Vannini M, et al. Clinical risk score to predict in-hospital mortality in COVID-19 patients: a retrospective cohort study. BMJ Open. 2020 Sep 25;10(9):e040729.
https://www.doi.org/10.1136/bmjopen-2020-040729
http://www.ncbi.nlm.nih.gov/pubmed/32978207?tool=bestpractice.com
3F:一种基于以下三个临床特征的死亡率预测模型:年龄、最低血氧饱和度和接诊类型(即住院患者与门诊患者以及远程医疗患者)。一项研究发现,该模型应用于 COVID-19 患者的回顾性和前瞻性数据集时,表现出很高的准确性。[1087]Yadaw AS, Li YC, Bose S, et al. Clinical features of COVID-19 mortality: development and validation of a clinical prediction model. Lancet Digit Health. 2020 Oct;2(10):e516-25.
https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30217-X/fulltext
http://www.ncbi.nlm.nih.gov/pubmed/32984797?tool=bestpractice.com
4C:英国一项针对 COVID-19 成人住院患者前瞻性队列研究开发和验证的一类评分。这一评分会用到入院时获得的患者人口统计学资料、临床观察发现和血液参数,能准确将患者分类为死亡风险低、中、高或极高。该评分要优于其他风险分层工具,展现出了临床决策实用性,且性能与更复杂的模型相近。[1088]Knight SR, Ho A, Pius R, et al. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO clinical characterisation protocol: development and validation of the 4C mortality score. BMJ. 2020 Sep 9;370:m3339.
https://www.bmj.com/content/370/bmj.m3339
http://www.ncbi.nlm.nih.gov/pubmed/32907855?tool=bestpractice.com
QCOVID:一种新型临床风险预测算法,根据年龄、种族、剥夺、体重指数和一系列合并症,估计住院和死亡风险。一项基于人群的队列研究发现,该算法实际应用表现良好,对死亡和住院具有高度可辨性。[1089]Clift AK, Coupland CAC, Keogh RH, et al. Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study. BMJ. 2020 Oct 20;371:m3731.
https://www.bmj.com/content/371/bmj.m3731
http://www.ncbi.nlm.nih.gov/pubmed/33082154?tool=bestpractice.com