Effect of mortality from COVID-19 on inpatient outcomes

When hospitals first encountered coronavirus disease 2019 (COVID-19), there was a dearth of therapeutic options and nearly 1 in 3 patients died from the disease. By the summer of 2020, as deaths from the disease declined nationally, multiple single-center studies began to report declining mortality of patients with COVID-19. To evaluate the effect of COVID-19 on hospital-based mortality, we searched the Vizient Clinical Data Base for outcomes data from approximately 600 participating hospitals, including 130 academic medical centers, from January 2017 through December 2020. More than 32 million hospital admissions were included in the analysis. After an initial spike, mortality from COVID-19 declined in all regions of the country to under 10% by June 2020 and remained constant for the remainder of the year. Despite this, inpatient, all-cause mortality has increased since the beginning of the pandemic, even those without respiratory failure. Inpatient mortality has particularly increased in elderly patients and in those requiring intubation for respiratory failure. Since June 2020, COVID-19 kills one in every 10 patients admitted to the hospital with this diagnosis. The addition of this new disease has raised overall hospital mortality especially those who require intubation for respiratory failure.     

Renal NF-κB activation impairs uric acid homeostasis to promote tumor-associated mortality independent of wasting

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Economic Status and Mortality in Patients with Alzheimer's Disease in Japan: The Longevity Improvement and Fair Evidence Study

ObjectivesAs more countries are implementing measures to address Alzheimer’s disease (AD), it is essential to update the available knowledge on the relationship between economic status and mortality in patients with AD. This study examined the influence of economic status on mortality in Japanese individuals with AD using a medical claims dataset.DesignThis was a retrospective cohort study.Setting and ParticipantsMedical claims data from April 2014 to March 2019 were obtained from 13 local cities participating in the Longevity Improvement and Fair Evidence study. The inclusion criteria were patients aged 65 years and older who were newly diagnosed with AD during the study period.MethodsThe outcome was death during the follow-up period. We assessed economic status by household income (middle to high income and low income); data were obtained from the use of the Medical Expenditure Ceiling Application and Standard Copayment Reduction Card (fee reduction card) when receiving an AD diagnosis, as an indicator of low-income status. We performed multivariate Cox proportional hazards analyses to examine the relationship between economic status and mortality; the model was adjusted for age, sex, the Charlson comorbidity index, and antidementia drug use.ResultsWe identified 39,081 newly diagnosed patients with AD from the Longevity Improvement and Fair Evidence study database (mean age, 83.6 years; female, 67.1%). Of these, 3189 individuals were identified as having a low-income status. After adjusting for possible confounders, low-income status was associated with mortality (hazard ratio, 1.95; 95% confidence interval, 1.84–2.07).Conclusions and ImplicationsLow-income status was associated with substantially poorer prognoses in new AD cases, indicating a need for a thorough examination of medical and nursing care services utilized by low-income individuals with AD and to explore improvement strategies.     

Redefining “low risk”: Outcomes of surgical aortic valve replacement in low-risk patients in the transcatheter aortic valve replacement era

ObjectivesGuidelines suggest aortic valve replacement (AVR) for low-risk asymptomatic patients. Indications for transcatheter AVR now include low-risk patients, making it imperative to understand state-of-the-art surgical AVR (SAVR) in this population. Therefore, we compared SAVR outcomes in low-risk patients with those expected from Society of Thoracic Surgeons (STS) models and assessed their intermediate-term survival.MethodsFrom January 2005 to January 2017, 3493 isolated SAVRs were performed in 3474 patients with STS predicted risk of mortality <4%. Observed operative mortality and composite major morbidity or mortality were compared with STS-expected outcomes according to calendar year of surgery. Logistic regression analysis was used to identify risk factors for these outcomes. Patients were followed for time-related mortality.ResultsWith 15 observed operative deaths (0.43%) compared with 55 expected (1.6%), the observed:expected ratio was 0.27 for mortality (95% confidence interval [CI], 0.14-0.42), stroke 0.65 (95% CI, 0.41-0.89), and reoperation 0.50 (95% CI, 0.42-0.60). Major morbidity or mortality steadily declined, with probabilities of 8.6%, 6.7%, and 5.2% in 2006, 2011, and 2016, respectively, while STS-expected risk remained at approximately 12%. Mitral valve regurgitation, ventricular hypertrophy, pulmonary, renal, and hepatic failure, coronary artery disease, and earlier surgery date were residual risk factors. Survival was 98%, 91%, and 82% at 1, 5, and 9 years, respectively, superior to that predicted for the US age-race-sex–matched population.ConclusionsSTS risk models overestimate contemporary SAVR risk at a high-volume center, supporting efforts to create a more agile quality assessment program. SAVR in low-risk patients provides durable survival benefit, supporting early surgery and providing a benchmark for transcatheter AVR.     

Risk as a pattern over time: Delineation of time-dependent risk factors in biological,psychological, and social variables in cancer patients

Background

Survival in cancer patients is associated with a multitude of biological, social, and psychological factors. Although it is well established that all these factors add to overall mortality, it is not well understood how the predictive power of these parameters changes in a comprehensive model and over time.

Methods

Patients who attended the authors’ outpatient clinic were invited to participate. The authors followed 5180 mixed cancer patients (51.1% female; mean age, 59.1 years [SD = 13.8]) for up to 16 years and analyzed biological (age, sex, cancer site, anemia), psychological (anxiety, depression), and social variables (marital status, education, employment status) potentially predicting overall survival in a Cox proportional hazards model.

Results

The median survival time for the entire sample was 4.3 years (95% confidence interval, 4.0–4.7). The overall survival probabilities for 1 and 10 years were 76.8% and 38.0%, respectively. Following an empirical approach, the authors split the time interval into five periods: acute, subacute, short-term, medium-term, and long-term. A complex pattern of variables predicted overall survival differently in the five periods. Biological parameters were important throughout most of the time, social parameters were either time-independent predictors or tended to be more important in the longer term. Of the psychological parameters, only depression was a significant predictor and lost its predictive power in the long-term.

Conclusions

The findings of this study allow the development of comprehensive patient-specific models of risk and resilience factors addressing biopsychosocial needs of cancer patients, paving the way for a personalized treatment plan that goes beyond biomedical cancer care.