|
Journal of Zhejiang University SCIENCE B
ISSN 1673-1581(Print), 1862-1783(Online), Monthly
2021 Vol.22 No.4 P.318-329
Development and validation of an individualized nomogram for early prediction of the duration of SARS-CoV-2 shedding in COVID-19 patients with non-severe disease
Abstract: With the number of cases of coronavirus disease-2019 (COVID-19) increasing rapidly, the World Health Organization (WHO) has recommended that patients with mild or moderate symptoms could be released from quarantine without nucleic acid retesting, and self-isolate in the community. This may pose a potential virus transmission risk. We aimed to develop a nomogram to predict the duration of viral shedding for individual COVID-19 patients. This retrospective multicentric study enrolled 135 patients as a training cohort and 102 patients as a validation cohort. Significant factors associated with the duration of viral shedding were identified by multivariate Cox modeling in the training cohort and combined to develop a nomogram to predict the probability of viral shedding at 9, 13, 17, and 21 d after admission. The nomogram was validated in the validation cohort and evaluated by concordance index (C-index), area under the curve (AUC), and calibration curve. A higher absolute lymphocyte count (P=0.001) and lymphocyte-to-monocyte ratio (P=0.013) were correlated with a shorter duration of viral shedding, while a longer activated partial thromboplastin time (P=0.007) prolonged the viral shedding duration. The C-indices of the nomogram were 0.732 (95% confidence interval (CI): 0.685‒0.777) in the training cohort and 0.703 (95% CI: 0.642‒0.764) in the validation cohort. The AUC showed a good discriminative ability (training cohort: 0.879, 0.762, 0.738, and 0.715 for 9, 13, 17, and 21 d; validation cohort: 0.855, 0.758, 0.728, and 0.706 for 9, 13, 17, and 21 d), and calibration curves were consistent between outcomes and predictions in both cohorts. A predictive nomogram for viral shedding duration based on three easily accessible factors was developed to help estimate appropriate self-isolation time for patients with mild or moderate symptoms, and to control virus transmission.
Key words: Coronavirus disease-2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Duration of viral shedding; Nomogram
1河南科技大学农业装备工程学院,中国洛阳市,471003
2河南科技大学信息工程学院,中国洛阳市,471003
3南开大学人工智能学院,中国天津市,300071
摘要:现有大多数关于有限状态自动机(finite state machines, FSM)状态空间的优化方法不便甚至不能给出优化的数学意义。本文将FSM视为逻辑动态系统,借鉴控制论中动态系统平衡点的概念,引入t-等价状态和t-源等价状态概念。基于近年提出的FSM状态转移动力学方程,得到t-等价状态和t-源等价状态的数学描述(该数学描述可类比于控制论中关于动态系统平衡点的充要条件),进而给出该优化问题的数学解释。基于这些数学描述,设计了求解FSM所有t-等价状态和t-源等价状态的两种方法。此外,找到降低FSM状态空间的两种路径。可不借助计算机,仅用纸笔以数学推演方式实现。并且,为使所设计的方法借助计算机能完全以无人值守方式运行,提出一个开放性问题。最后,采用实际语言模型验证了结论的正确性和有效性。
关键词组:
References:
Open peer comments: Debate/Discuss/Question/Opinion
<1>
DOI:
10.1631/jzus.B2000608
CLC number:
Download Full Text:
Downloaded:
2532
Download summary:
<Click Here>Downloaded:
1884Clicked:
5608
Cited:
0
On-line Access:
2024-08-27
Received:
2023-10-17
Revision Accepted:
2024-05-08
Crosschecked:
2021-03-16