Triage Strategies Based on C-Reactive Protein Levels and SARS-CoV-2 Tests among Individuals Referred with Suspected COVID-19: A Prospective Cohort Study

Pentraxin-3 and Other Inflammatory Markers for an Infected Diabetic Foot Ulcer Diagnosis: A Prospective Study

Strategies have been researched and implemented to reduce the number of people with diabetic foot ulcers (DFUs). One problem is the accurate assessment of DFU severity, which is the main factor in resource allocation and treatment choice. The primary objective of this study was to assess pentraxin-3 as a biomarker of an infected DFU (IDFU), the limb amputation level prognosis, and patient survival. The secondary objectives were to evaluate and compare other markers, including white blood cells (WBCs), C-reactive protein (CRP), the erythrocyte sedimentation rate (ESR), and procalcitonin (PCT), for identifying IDFUs. Over a period of two years, 145 patients were followed; 131 of these were analyzed for this study. Pentraxin-3 was found to be a good predictor of death (p = 0.047). A comparison between IDFUs and DFUs revealed the following differences: PCT had the highest AUROC of 0.91, sensitivity of 93.7, and specificity of 83.3%. CRP had a cutoff value of 226 mg/L, an AUROC of 0.89, a sensitivity of 95.5%, and a specificity of 83.3%. Fibrinogen had an AUROC of 0.87 at a cutoff value of 5.29 g/L, with a good sensitivity and specificity of 85% and 87%, respectively. ESR had a cutoff value of 46 mm/h, an AUROC of 85%, a sensitivity of 83.7%, and a specificity of 83.3%. Pentraxin-3 showed promising results in predicting IDFUs and DFUs, and it served as a marker for the risk of death in IDFU patients during the 6 month follow-up. Other markers, including CRP, PCT, ESR, and fibrinogen, were more effective in differentiating between IDFUs and DFUs.

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China

Purpose

To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19).

Patients and Methods

A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan–Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19.

Results

A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively.

Conclusion

We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.