Predicting Progression of COVID-19 Infection to Prioritize Medical Resource Allocation: A Novel Triage Model Based on Patient Characteristics and Symptoms at Presentation

Tu, Yuexing; Zhou, Xianlong; Shao, Lina; Zheng, Jiayin; Wang, Jiafeng; Wang, Yixin; Tong, Weiwei; Wang, Mingshan; Wu, Jia; Zhu, Jiangcheng; Yan, Rong; Ji, Y.; Chen, Legao; Zhu, Di; Wang, Huafang; Chen, Sheng; Liu, Renyang; Lin, Jingyang; Zhang, Jun; Huang, Haijun; Zhao, Yan; Ge, Minghua

doi:10.3389/fpubh.2021.610280

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…In the present study, we demonstrated two simple methods for predicting the development of illness among mild/moderate patients with COVID-19 that do not require blood sampling: DOAT and DOATS scores. Tu et al developed a model predicting the progression of COVID-19 consisting of nine items: sex, age, and the presence of a fever, hypertension, cardio-cerebrovascular disease, dyspnea, cough, and myalgia [ 14 ]. Their model also consisted of clinical characteristics only, with no laboratory data involved.…”

Section: Discussionmentioning

confidence: 99%

Development and external validation of the DOAT and DOATS scores: simple decision support tools to identify disease progression among nonelderly patients with mild/moderate COVID-19

Shibata,

Omae,

Minemura

et al. 2023

BMC Pulm Med

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Background During the fifth wave of the coronavirus disease 2019 (COVID-19) pandemic in Japan, which took place between June and September 2021, a significant number of COVID-19 cases with deterioration occurred in unvaccinated individuals < 65 years old. However, the risk factors for COVID-19 deterioration in this specific population have not yet been determined. This study developed a prediction method to identify COVID-19 patients < 65 years old who are at a high risk of deterioration. Methods This retrospective study analyzed data from 1,675 patients < 65 years old who were admitted to acute care institutions in Fukushima with mild-to-moderate-1 COVID-19 based on the Japanese disease severity criteria prior to the fifth wave. For validation, 324 similar patients were enrolled from 3 hospitals in Yamagata. Logistic regression analyses using cluster-robust variance estimation were used to determine predictors of disease deterioration, followed by creation of risk prediction scores. Disease deterioration was defined as the initiation of medication for COVID-19, oxygen inhalation, or mechanical ventilation starting one day or later after admission. Results The patients whose condition deteriorated (8.6%) tended to be older, male, have histories of smoking, and have high body temperatures, low oxygen saturation values, and comorbidities, such as diabetes/obesity and hypertension. Stepwise variable selection using logistic regression to predict COVID-19 deterioration retained comorbidities of diabetes/obesity (DO), age (A), body temperature (T), and oxygen saturation (S). Two predictive scores were created based on the optimism-corrected regression coefficients: the DOATS score, including all of the above risk factors, and the DOAT score, which was the DOATS score without oxygen saturation. In the original cohort, the areas under the receiver operating characteristic curve (AUROCs) of the DOATS and DOAT scores were 0.81 (95% confidence interval [CI] 0.77–0.85) and 0.80 (95% CI 0.76–0.84), respectively. In the validation cohort, the AUROCs for each score were both 0.76 (95% CI 0.69–0.83), and the calibration slopes were both 0.80. A decision curve analysis confirmed the clinical practicability of both scores in the validation cohort. Conclusions We established two prediction scores that can quickly evaluate the risk of COVID-19 deterioration in mild/moderate patients < 65 years old.

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Section: Discussionmentioning

confidence: 99%

Development and external validation of the DOAT and DOATS scores: simple decision support tools to identify disease progression among nonelderly patients with mild/moderate COVID-19

Shibata,

Omae,

Minemura

et al. 2023

BMC Pulm Med

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“…Compared with the eastern region, the number of general practitioners in the central and western regions is small. Meanwhile, although the number of general practitioners per 10,000 people in the eastern, central and western regions is on the rise, there are obvious regional differences between eastern, central, and western regions (Tu et al, 2021). The most widely used neural network model is the back propogation algorithm (BP) neural network.…”

Section: Introductionmentioning

confidence: 99%

“…per 10,000 people in the eastern, central and western regions is on the rise, there are obvious regional differences between eastern, central, and western regions (Tu et al, 2021). The most widely used neural network model is the back propogation algorithm (BP) neural network.…”

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confidence: 99%

Improved grey‐level correlation feature and neural network model for medical resource requirement prediction

Huang

2021

Expert Systems

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Although the country's general practitioner training system is constantly improving, problems such as large requirement gaps, low specialization, unfair resource allocation, and uneven regional distribution still exist. In order to promote the improvement of the general practitioner system, to solve the problems of low accuracy and poor robustness of the existing models for medical resource requirement prediction, this paper proposes a combination of grey‐scale prediction features and back propogation algorithm (BP) neural network medical resource requirement prediction algorithm. The algorithm first uses the principal component analysis algorithm to solve the principal components of the medical resource requirement influencing factors, then extracts the equal‐dimensional dynamic grey‐level optimization model grey prediction features of the principal component score, and finally inputs the features into the BP neural network to complete the medical resource requirement prediction. Subsequently, a large number of comparative experiments were carried out on the algorithm proposed in this paper using the medical resource requirement of a certain province as a data set. The experimental results have shown that the comprehensive improvement model proposed in this paper has the best effect in predicting the requirement of medical resources, which contains strong robustness and stability. The algorithm is suitable for the needs of medical resources at this stage. Predicting on the above has strong practical significance in many scenarios by using the proposed algorithm.

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Development and external validation of the DOAT and DOATS scores: simple decision support tools to identify disease progression among nonelderly patients with mild/moderate COVID-19

Shibata

Minemura

Suzuki

et al. 2021

Preprint

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BACKGROUND: Due to the dissemination of vaccination against severe acute respiratory syndrome coronavirus 2 in the elderly, the virus-susceptible subjects have shifted to unvaccinated non-elderlies. The risk factors of COVID-19 deterioration in non-elderly patients without respiratory failure have not yet been determined. This study was aimed to create simple predicting method to identify such patients who have high risk for exacerbation. METHODS: We analyzed the data of 1675 patients aged under 65 years who were admitted to hospitals with mild-to-moderate COVID-19. For validation, 324 similar patients were enrolled. Disease progression was defined as administration of medication, oxygen inhalation and mechanical ventilator starting one day or longer after admission. RESULTS: The patients who exacerbated tended to be older, male, had histories of smoking, and had high body temperatures, lower oxygen saturation, and comorbidities such as diabetes/obesity and hypertension. Stepwise logistic regression analyses revealed that comorbidities of diabetes/obesity, age ≥ 40 years, body temperature ≥ 38 degree, and oxygen saturation < 96% (DOATS) were independent risk factors of worsening COVID-19. As a result two predictive scores were created: DOATS score, which includes all the above risk factors; and DOAT score, which includes all factors except for oxygen saturation. In the original cohort, the areas under the receiver operating characteristic curve of the DOATS and DOAT scores were 0.789 and 0.771, respectively. In the validation, the areas were 0.702 and 0.722, respectively. CONCLUSION: We established two simple prediction scores that can quickly evaluate the risk of progression of COVID-19 in non-elderly, mild/moderate patients.

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Predicting Progression of COVID-19 Infection to Prioritize Medical Resource Allocation: A Novel Triage Model Based on Patient Characteristics and Symptoms at Presentation

Cited by 3 publications

References 17 publications

Development and external validation of the DOAT and DOATS scores: simple decision support tools to identify disease progression among nonelderly patients with mild/moderate COVID-19

Development and external validation of the DOAT and DOATS scores: simple decision support tools to identify disease progression among nonelderly patients with mild/moderate COVID-19

Improved grey‐level correlation feature and neural network model for medical resource requirement prediction

Development and external validation of the DOAT and DOATS scores: simple decision support tools to identify disease progression among nonelderly patients with mild/moderate COVID-19

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