Association Between High-Sensitivity Troponin T on Admission and Organ Dysfunction During Hospitalization in Patients Aged 80 Years and Older with Hip Fracture: A Single-Centered Prospective Cohort Study.

BACKGROUND: Prognostic evaluation of elderly patients with hip fracture is an issue that has been highly concerned by clinicians. Only a few studies have focused on organ dysfunction after hip fracture in the elderly. This study aimed to investigate the association between high-sensitivity troponin T (hs-TnT) at admission and organ dysfunction during hospitalization in elderly patients with hip fracture. METHODS: We enrolled 168 patients with hip fracture who were aged 80 years and older at Geriatric Orthopaedic Center of Sichuan Provincial Orthopedic Hospital between January 2020 and August 2020. Baseline characteristics, perioperative information, and short-term clinical outcomes were analyzed. RESULTS: Of the 208 patients admitted during the study period, 168 met the inclusion criteria; of these, 91 (54.2%) had higher hs-TnT than the 99th percentile in the normal population. After adjustment for confounders, elevated hs-TnT was independently associated with multiple organ dysfunction syndrome in the elderly (MODSE) (adjusted OR, 5.76; 95% CI, 1.74-19.10; P = 0.004), heart dysfunction (adjusted OR, 7.48; 95% CI, 2.17-25.82; P = 0.001), MODS severity score > 3 (adjusted OR, 5.22; 95% CI, 1.32-20.60; P = 0.018), and length of hospital stay > 14 days (adjusted OR, 2.38; 95% CI, 1.05-5.36; P = 0.037). CONCLUSION: Increased hs-TnT on admission is an independent risk factor for MODSE after hip fracture in patients aged 80 years and older. Effective measures should be applied to avoid progression of MODSE from pre-failure stage to failure stage.

Impaired immune and coagulation systems may be early risk factors for COVID-19 patients: A retrospective study of 118 inpatients from Wuhan, China.

The coronavirus disease 2019 (COVID-19) outbreak has become a global health threat and will likely be one of the greatest global challenges in the near future. The battle between clinicians and the COVID-19 outbreak may be a "protracted war."The objective of this study was to investigate the risk factors for in-hospital mortality in patients with COVID-19, so as to provide a reference for the early diagnosis and treatment.This study retrospectively enrolled 118 patients diagnosed with COVID-19, who were admitted to Eastern District of Renmin Hospital of Wuhan University from February 04, 2020 to March 04, 2020. The demographics and laboratory data were collected and compared between survivors and nonsurvivors. The risk factors of in-hospital mortality were explored by univariable and multivariable logistic regression to construct a clinical prediction model, the prediction efficiency of which was verified by receiver-operating characteristic (ROC) curve.A total of 118 patients (49 males and 69 females) were included in this study; the results revealed that the following factors associated with in-hospital mortality: older age (odds ratio [OR] 1.175, 95% confidence interval [CI] 1.073-1.287, P = .001), neutrophil count greater than 6.3 × 10 cells/L (OR 7.174, (95% CI 2.295-22.432, P = .001), lymphocytopenia (OR 0.069, 95% CI 0.007-0.722, P = .026), prothrombin time >13 seconds (OR 11.869, 95% CI 1.433-98.278, P = .022), D-dimer >1 mg/L (OR 22.811, 95% CI 2.224-233.910, P = .008) and procalcitonin (PCT) >0.1 ng/mL (OR 23.022, 95% CI 3.108-170.532, P = .002). The area under the ROC curve (AUC) of the above indicators for predicting in-hospital mortality were 0.808 (95% CI 0.715-0.901), 0.809 (95% CI 0.710-0.907), 0.811 (95% CI 0.724-0.898), 0.745 (95% CI 0.643-0.847), 0.872 (95% CI 0.804-0.940), 0.881 (95% CI 0.809-0.953), respectively. The AUC of combined diagnosis of these aforementioned factors were 0.992 (95% CI 0.981-1.000).In conclusion, older age, increased neutrophil count, prothrombin time, D-dimer, PCT, and decreased lymphocyte count at admission were risk factors associated with in-hospital mortality of COVID-19. The prediction model combined of these factors could improve the early identification of mortality risk in COVID-19 patients.