Development and validation of a simple-to-use nomogram to predict the deterioration and survival of patients with COVID-19

Abstract

Background COVID-19 pandemic has forced physicians to quickly determine the patient’s condition and choose treatment strategies. This study aimed to build and validate a simple tool that can quickly predict the deterioration and survival of COVID-19 patients. Methods A total of 351 COVID-19 patients admitted to the Third People’s Hospital of Yichang between 9 January to 25 March 2020 were retrospectively analyzed. Patients were randomly grouped into training (n = 246) or a validation (n = 105) dataset. Risk factors associated with deterioration were identified using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression. The factors were then incorporated into the nomogram. Kaplan-Meier analysis was used to compare the survival of patients between the low- and high-risk groups divided by the cut-off point. Results The least absolute shrinkage and selection operator (LASSO) regression was used to construct the nomogram via four parameters (white blood cells, C-reactive protein, lymphocyte≥0.8 × 109/L, and lactate dehydrogenase ≥400 U/L). The nomogram showed good discriminative performance with the area under the receiver operating characteristic (AUROC) of 0.945 (95% confidence interval: 0.91–0.98), and good calibration (P = 0.539). Besides, the nomogram showed good discrimination performance and good calibration in the validation and total cohorts (AUROC = 0.979 and AUROC = 0.954, respectively). Decision curve analysis demonstrated that the model had clinical application value. Kaplan-Meier analysis illustrated that low-risk patients had a significantly higher 8-week survival rate than those in the high-risk group (100% vs 71.41% and P < 0.0001). Conclusion A simple-to-use nomogram with excellent performance in predicting deterioration risk and survival of COVID-19 patients was developed and validated. However, it is necessary to verify this nomogram using a large-scale multicenter study.

Computed tomography‐defined body composition as prognostic markers for unfavourable outcomes and in‐hospital mortality in coronavirus disease 2019

“…Thus, a short‐term mortality of up to 20% was reported in COVID‐19 patients of the ICU 1–6 . Already established prognosis parameters are age, male sex, and shorter time period between symptom onset and the admission to the emergency room 1–6,27–32 . Moreover, the consolidation extension of CT images is also considered prognostic relevant and can be considered the most important factor derived from radiological images to date 2 .…”

“…31,32 A recent study could show that a score based on serologically parameters, white blood cells, C-reactive protein, lymphocyte ≥0.8 × 10 9 /L, and lactate dehydrogenase ≥400 U/L was highly accurate with an area under the curve of 0.95. 32 Of great interest could be whether imaging biomarkers could provide complementary information additionally to serologically parameters. There is definite need of further studies to combine both prognostic fields to elucidate this hypothesis.…”

“…[1][2][3][4][5][6] Already established prognosis parameters are age, male sex, and shorter time period between symptom onset and the admission to the emergency room. [1][2][3][4][5][6][27][28][29][30][31][32] Moreover, the consolidation extension of CT images is also considered prognostic relevant and can be considered the most important factor derived from radiological images to date. 2 The present analysis can support the importance of novel body composition CT parameters for prognostic purposes.…”

Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model

“…According to the WHO guidelines [ 18 ], the cases were classified as mild, moderate, severe, or critical [including acute respiratory distress syndrome (ARDS), sepsis, septic shock]. As the primary outcome, deterioration refers to the progression from mild or moderate to severe, critical or mortal [ 9 , 10 ].…”

Analyzing the Death Ratio of Covid Patients using Multiple Logistic Regression in Comparison with Lasso Regression for Improving Accuracy

“…Another article is revised from Science Direct and has a citation of 42 and discusses ML approaches in covid19 for survival analysis (Nemati, Ansary, and Nemati 2020). Another article has a citation of 11 and which is cited in Springer and it discusses how simple-to-use is used to predict deterioration and and survival of patients with covid19 (Zeng et al 2021). One more article (Zhou et al 2020) has 8 citations and is cited in Springer and discusses exploiting risks of covid19 patients joining in hospitals.…”