Abstract
Background At present, the death cases with SARS-CoV-2 pneumonia are continuing to increase globally. However, the information on death cases and predictive methods are substantial lacking. We aimed to develop a nomogram, which was validated by both internal and external cohorts, for early predicting mortality in hospitalized patients with SARS-CoV-2 pneumonia.Methods We retrospectively collected data on 1,540 patients confirmed SARS-CoV-2 pneumonia from two hospitals. Multivariate logistic regression analysis was performed to examine factors associated with in-hospital mortality. We investigated the mortality related risk factors and their weights, thereafter designed and validated a predictive nomogram model to facilitate early discrimination of in-hospital death. We assessed the nomogram performance by examining calibration (calibration plots and Hosmer–Lemeshow calibration test) and discrimination (AUROC). We also plotted survival curves and decision curves to evaluate the clinical usefulness of the nomogram.Results In the 1,540 patients from two centers, 248 cases died (16.1%). In the predictive nomogram calculated by a multivariate logistic regression analysis, eight independent risk factors associated mortality included age ≥ 60 years (odd ratio(OR) = 2.840; 95%CI, 1.467–5.495; P = 0.002), respiratory rate ≥ 30 breaths per minute (OR = 3.308; 95%CI, 1.408–7.770; P = 0.006), neutrophil count ≥ 7 × 109/L (OR = 3.084; 95%CI, 1.667–5.707; P < 0.001), lymphocyte count ≤ 0.8 × 109/ L (OR = 4.688; 95%CI, 2.500-8.791; P < 0.001), d-dimer ≥ 1.5 µg/mL(OR = 2.159; 95%CI, 1.169–3.989; P = 0.014), lactate dehydrogenase ≥ 350U/L(OR = 4.385; 95%CI, 2.299–8.362; P < 0.001), procalcitonin ≥ 0.1 ng/mL(OR = 4.972; 95%CI, 2.537–9.746; P < 0.001), and presence of myocardial injury (OR = 2.289; 95%CI, 1.260–4.160; P = 0.007) on admission. Calibration curves showed good fitting of the nomogram model with no statistical significance (P = 0.740) by Hosmer-Lemeshow test. This predictive nomogram had better predictive ability than CURB-65 score in training set (AUROC = 0.956 vs 0.828,P < 0.001). The good predictive performance of the nomogram is suggested by calibration, discrimination, and survival curve analysis, whether in the training, internal or external validation set. The decision curve analysis showed that predicting mortality risk applying this nomogram would be better than having all patients or none patients.Conclusions This nomogram is a reliable prognostic method that can accurately and early predict in-hospital mortality in patients with SARS-CoV-2 pneumonia. It can guide clinicians to improve their abilities to evaluate patient prognosis, enhance patient stratification, make earlier and reasonable decisions.Trail registration: This is a retrospective observational study without a trial registration number.
Incidence and risk factors for hypoglycemia in patients with hepatocellular carcinoma
