Importance: Heart injury can be easily induced by viral infection such as adenovirus and enterovirus. However, whether coronavirus disease 2019 (COVID-19) causes heart injury and hereby impacts mortality has not yet been fully evaluated. Objective: To explore whether heart injury occurs in COVID-19 on admission and hereby aggravates mortality later. Design, Setting, and Participants: A single-center retrospective cohort study including 188 COVID-19 patients admitted from December 25, 2019 to January 27, 2020 in Wuhan Jinyintan Hospital, China; follow up was completed on February 11, 2020. Exposures: High levels of heart injury indicators on admission (hs-TNI; CK; CK-MB; LDH; α-HBDH). Main Outcomes and Measures: Mortality in hospital and days from admission to mortality (survival days). Results: Of 188 patients with COVID-19, the mean age was 51.9 years (standard deviation: 14.26; range: 21∼83 years) and 119 (63.3%) were male. Increased hs-TnI levels on admission tended to occur in older patients and patients with comorbidity (especially hypertension). High hs-TnI on admission (≥ 6.126 pg/mL), even within the clinical normal range (0∼28 pg/mL), already can be associated with higher mortality. High hs-TnI was associated with increased inflammatory levels (neutrophils, IL-6, CRP, and PCT) and decreased immune levels (lymphocytes, monocytes, and CD4 ⁺ and CD8 ⁺ T cells). CK was not associated with mortality. Increased CK-MB levels tended to occur in male patients and patients with current smoking. High CK-MB on admission was associated with higher mortality. High CK-MB was associated with increased inflammatory levels and decreased lymphocytes. Increased LDH and α-HBDH levels tended to occur in older patients and patients with hypertension. Both high LDH and α-HBDH on admission were associated with higher mortality. Both high LDH and α-HBDH were associated with increased inflammatory levels and decreased immune levels. hs-TNI level on admission was negatively correlated with survival days ( r = -0.42, 95% CI= -0.64∼-0.12, P=0.005). LDH level on admission was negatively correlated with survival days ( r = -0.35, 95% CI= -0.59∼-0.05, P=0.022). Conclusions and Relevance: Heart injury signs arise in COVID-19, especially in older patients, patients with hypertension and male patients with current smoking. COVID-19 virus might attack heart via inducing inflammatory storm. High levels of heart injury indicators on admission are associated with higher mortality and shorter survival days. COVID-19 patients with signs of heart injury on admission must be early identified and carefully managed by cardiologists, because COVID-19 is never just confined to respiratory injury. Key points: Question: Does coronavirus disease 2019 (COVID-19) cause heart injury and hereby impact mortality? Findings: In this retrospective cohort study including 188 patients with COVID-19, patients with high levels of high-sensitivity cardiac troponin I (hs-TNI) on admission had significantly higher mortality (50.0%) than patients with moderate or low levels of hs-TNI (10.0% or 9.1%). hs-TNI level on admission was significantly negatively correlated with survival days ( r = -0.42, 95% CI= -0.64∼-0.12, P=0.005). Meaning: COVID-19 patients with signs of heart injury on admission must be early identified and carefully managed by cardiologists, in order to maximally prevent or rescue heart injury-related mortality in COVID-19.

Recent studies showed that overwhelming inflammatory response mediated by the toll-like receptor (TLR)-related pathway was important in the development of acute lung injury (ALI). The aim of this study was to determine whether common genetic variation in four genes of the TLR signaling pathway were associated with sepsis-induced ALI susceptibility and risk of death in Chinese Han population.

Monocytic myeloid-derived suppressor cells (M-MDSCs), granulocytic MDSC (G-MDSCs) and regulatory T cells (Tregs) inhibit adaptive anti-tumor immunity and undermine the efficacy of anti-PD-1 therapy. However, the impact of anti-PD-1 treatment on these immunosuppressive cells has not been clearly defined in non-small cell lung cancer (NSCLC). In this retrospective study, 27 advanced NSCLC patients were divided into partial response (PR), stable disease (SD), and progressive disease (PD) groups. The impact of anti-PD-1 therapy on circulating Tregs, G-MDSCs, and M-MDSCs was assessed by flow cytometer. Here, we found that anti-PD-1 treatment boosted circulating Tregs levels, which presented the most remarkable augment during the first two therapeutic cycles, in NSCLC patients. In contrast, anti-PD-1 therapy did not overall change G-MDSCs and M-MDSCs levels. However, the PR group had a higher baseline level of M-MDSCs, which exhibited a significant decrease after the first cycle of anti-PD-1 treatment. Besides, M-MDSCs levels in the PR group were maintained at a low level in the following therapeutic cycles. Consistently, Tregs levels robustly increased in the syngeneic tumor model after anti-mouse PD-1 Ab treatment. Accordingly, M-MDSCs neutralization by anti-mouse ly6c Ab enhanced the anti-tumor efficacy of anti-PD-1 therapy in mice. Finally, the decreased M-MDSCs levels were associated with the enhanced effector CD8+ T cells expansion in the PR group and mice. In conclusion, anti-PD-1 therapy upregulates Tregs levels in NSCLC patients, and the M-MDSC levels are associated with the anti-tumor efficacy of anti-PD-1 treatment. Neutralization of M-MDSCs may be a promising option to augment anti-PD-1 therapy efficacy in NSCLC.

Background: Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. Methods: Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). Results: The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. Conclusions: The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.

OBJECTIVE: To develop and validate a prognostic model for in-hospital mortality in COVID-19 patients using routinely collected demographic and clinical characteristics. DESIGN: Multicenter, retrospective cohort study. SETTING: Jinyintan Hospital, Union Hospital, and Tongji Hosptial in Wuhan, China. PARTICIPANTS: A pooled derivation cohort of 1008 COVID-19 patients from Jinyintan Hospital, Union Hospital in Wuhan and an external validation cohort of 1031 patients from Tongji Hospital in Wuhan, China. MAIN OUTCOME MEASURES: Outcome of interest was in-hospital mortality, treating discharged alive from hospital as the competing event. Fine-Gray models, using backward elimination for inclusion of predictor variables and allowing non-linear effects of continuous variables, were used to derive a prognostic model for predicting in-hospital mortality among COVID-19 patients. Internal validation was implemented to check model overfitting using bootstrap approach. External validation to a separate hospital was implemented to evaluate the generalizability of the model. RESULTS: The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (n=1008, 43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (n=1031, 47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted survival curves were close to the observed survival curves across patients with different risk profiles. CONCLUSIONS: The PLANS model based on the five routinely collected demographic and clinical characteristics (platelet count, lymphocyte count, age, neutrophil count, and sex) showed excellent discriminative and calibration accuracy in predicting in-hospital mortality in COVID-19 patients. This prognostic model would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.