Abstract
Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.
Highlights
The rapid spread of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a potentially fatal disease is a major and urgent threat to global health [1]
The inclusion criteria were as follows: [1] patients with a laboratory-confirmed COVID-19, which was achieved by real-time reverse transcription-polymerase chain reaction (RTPCR) assay of throat swab samples for COVID-19 according to the protocol established by the World Health Organization (WHO); and [2] patients received treatment at hospitals
Of 233 patients hospitalized for COVID-19 in the primary cohort, the mean age was 55.4 years ± 16.9, and 129 (55.4%) were male
Summary
The rapid spread of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a potentially fatal disease is a major and urgent threat to global health [1]. Some early warning models using machine learning for predicting COVID-19 patients at risk of developing a severe or critical condition have been reported [8,9,10,11,12,13,14]. Such models are usually assessed with statistical measures for discrimination and calibration. A model with better discrimination and calibration indicates a better guide to clinical management, whereas statistical measures fall short when we want to determine whether the risk model improves clinical decision-making [15]. It identifies risk models that can help us make better clinical decisions
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