Since the COVID-19 outbroke in Hubei, even with the optimal control in China, the cumulative confirmed cases in the globe had overpassed three million and the threat of coronavirus is still out there. In the patients who suffered COVID-19, CRP was increased in 86.22% of them, and ESR in 90.22%[13]. During the disease course, longitudinal evaluation of lymphocyte count dynamics and inflammatory indices, including LDH, CRP and IL-6 may help to identify cases with dismal prognosis and prompt intervention in order to improve outcomes[14]. Zhou et al. showed that increasing odds of in-hospital death associated with older age (odds ratio 1.10, 95% CI 1.03–1.17, per year increase; p = 0.0043), which could be the potential risk factor[15]. Another research demonstrated that elder age, underlying hypertension, high cytokine levels (IL-2R, IL-6, IL-10, and TNF-a), and high LDH level were significantly associated with severe COVID-19 on admission[16]. Until now, more and more independent risk factors have been determined and a number of systemic score systems have been built to analyze the status of disease progression and prevent severe outcomes. Dong et al. [6]reported a novel scoring model, named as CALL, established for disease condition prediction, which included comorbidity, age, lymphocyte, and LDH, with the AUC reaching 0.91 (95% CI 0.86–0.94). However, only one verification of this model has been made, the efficacy of it is doubted, and the number of patients is insufficient. Another model to early predict severe type of COVID-19 showed older age, higher LDH, CRP, RDW, DBIL, BUN, and lower ALB on admission correlated with higher odds of severe COVID-19, with the AUC reached 0.912 (95% CI 0.846–0.978) in the training set, and 0.853 (95% CI 0.790–0.916) in the validation set[5]. Nonetheless, the small sample size could be the deficiency of this model.
For the sake of controlling the major health incident and bettering the medical resource allocation, we extracted the clinical data of 590 cases from The Wuhan Jinyintan Hospital and the prediction model was been established, which included ESR, CDCS, Age, LDH, and CRP. Notably, CDCS, the Charlson/Deyo comorbidity score, is a method of categorizing comorbidities of patients based on the International Classification of Diseases (ICD) diagnosis codes found in administrative data, such as hospital abstracts data. Each comorbidity category has an associated weight (from 1 to 6), based on the adjusted risk of mortality or resource use, and the sum of all the weights results in a single comorbidity score for a patient. A score of zero indicates that no comorbidities were found. The higher the score, the more likely the predicted outcome will result in mortality or higher resource use. This scoring systems have been reported to be associated with overall survival in various types of cancer and the death rate of other morbidities, such as ischemic stroke, acute cholecystitis, acute hip fracture, and so forth[17–21]. Due to the efficacy of CDCS, we ground breakingly utilized this scoring system in our prediction model with meeting the rule of The TRIPOD Statement [22], which could also interpret the high mortality of COVID-19 with multiple comorbidities. Hereby, the five significant indices were overlapped by the LASSO and SVM analysis, which are machine learning used for classification and regression analysis in order to enhance the prediction accuracy and interpretability of the statistical model it produces. Then, the ROC, DCA, and Calibration were performed for performance assessment and the triple verification were applied. The predictive nomogram indicated that the possibility of the progression from common type to severe type could reach 50%, when the total points meet 197. Thereafter, the AUC of internal training set, testing set, and external testing set reached 0.822, 0.762, and 0.705, respectively.
However, there are still some limitation should be majorized in the future investigation. The AUC values are lower than 0.9 and more cases should be recruited to optimize our prediction model for more precise forecasting. And the data of patients were derived from Wuhan, Hubei Province, which means the situation outside Hubei Province could be distinct and multi-center analysis is urgently needed.