Post-Intensive Care Syndrome Among Survivors in a Safety Net Hospital in South Bronx: A Comparison of Patients With and Without Coronavirus Disease 2019.

Comparison of post-intensive care syndrome between critically ill survivors with or without coronavirus disease 2019 (CovP and CovN, respectively) showed that fewer CovP were able to return to work full time at >1 year and none at <1 year after discharge and that the majority of CovP survivors were able to work part time during both evaluation periods compared to CovN.

Role of a Chest X-ray Severity Score in a Multivariable Predictive Model for Mortality in Patients with COVID-19: A Single-Center, Retrospective Study.

Predicting the mortality risk of patients with Coronavirus Disease 2019 (COVID-19) can be valuable in allocating limited medical resources in the setting of outbreaks. This study assessed the role of a chest X-ray (CXR) scoring system in a multivariable model in predicting the mortality of COVID-19 patients by performing a single-center, retrospective, observational study including consecutive patients admitted with a confirmed diagnosis of COVID-19 and an initial CXR. The CXR severity score was calculated by three radiologists with 12 to 15 years of experience in thoracic imaging, based on the extent of lung involvement and density of lung opacities. Logistic regression analysis was used to identify independent predictive factors for mortality to create a predictive model. A validation dataset was used to calculate its predictive value as the AUROC. A total of 628 patients (58.1% male) were included in this study. Age (p < 0.001), sepsis (p < 0.001), S/F ratio (p < 0.001), need for mechanical ventilation (p < 0.001), and the CXR severity score (p = 0.005) were found to be independent predictive factors for mortality. We used these variables to develop a predictive model with an AUROC of 0.926 (0.891, 0.962), which was significantly higher than that of the WHO COVID severity classification, 0.853 (0.798, 0.909) (one-tailed p-value = 0.028), showing that our model can accurately predict mortality of hospitalized COVID-19 patients.

Cancer Impacts Prognosis on Mortality in Patients with Acute Heart Failure: Analysis of the EPICTER Study.

INTRODUCTION: Heart failure (HF) and cancer are currently the leading causes of death worldwide, with an increasing incidence with age. Little is known about the treatment received and the prognosis of patients with acute HF and a prior cancer diagnosis. OBJECTIVE: to determine the clinical characteristics, palliative treatment received, and prognostic impact of patients with acute HF and a history of solid tumor. METHODS: The EPICTER study ("Epidemiological survey of advanced heart failure") is a cross-sectional, multicenter project that consecutively collected patients admitted for acute HF in 74 Spanish hospitals. Patients were classified into two groups according to whether they met criteria for acute HF with and without solid cancer, and the groups were subsequently compared. A multivariable logistic regression analysis was conducted, using the forward stepwise method. A Kaplan-Meier survival analysis was performed to evaluate the impact of solid tumor on prognosis in patients with acute HF. RESULTS: A total of 3127 patients were included, of which 394 patients (13%) had a prior diagnosis of some type of solid cancer. Patients with a history of cancer presented a greater frequency of weight loss at admission: 18% vs. 12% (p = 0.030). In the cancer group, functional impairment was noted more frequently: 43% vs. 35%, p = 0.039). Patients with a history of solid cancer more frequently presented with acute HF with preserved ejection fraction (65% vs. 58%, p = 0.048) than reduced or mildly reduced. In-hospital and 6-month follow-up mortality was 31% (110/357) in patients with solid cancer vs. 26% (637/2466), p = 0.046. CONCLUSION: Our investigation demonstrates that in-hospital mortality and mortality during 6-month follow-up in patients with acute HF were higher in those subjects with a history of concomitant solid tumor cancer diagnosis.