Quality of care factors are more common among readmitted than among non-readmitted older patients suggesting potential for remedial strategies. Such strategies may still have limited effects as older, frail patients with advanced diseases and multimorbidity will likely retain a high propensity for readmission despite optimal care.

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Phase I studies are used to estimate the dose-toxicity profile of the drugs and to select appropriate doses for successive studies. However, literature on statistical methods used for phase I studies are extensive. The objective of this review is to provide a concise summary of existing and emerging techniques for selecting dosages that are appropriate for phase I cancer trials. Many advanced statistical studies have proposed novel and robust methods for adaptive designs that have shown significant advantages over conventional dose finding methods. An increasing number of phase I cancer trials use adaptive designs, particularly during the early phases of the study. In this review, we described nonparametric and algorithm-based designs such as traditional 3 + 3, accelerated titration, Bayesian algorithm-based design, up-and-down design, and isotonic design. In addition, we also described parametric model-based designs such as continual reassessment method, escalation with overdose control, and Bayesian decision theoretic and optimal design. Ongoing studies have been continuously focusing on improving and refining the existing models as well as developing newer methods. This study would help readers to assimilate core concepts and compare different phase I statistical methods under one banner. Nevertheless, other evolving methods require future reviews.

Major adverse cardiovascular and cerebrovascular events (MACCE) is an important cause of morbidity and mortality during perioperative period. In this study, we looked for national trends in perioperative MACCE and its components as well as cancer types associated with high rates of perioperative MACCE during major cancer surgeries. This study was a retrospective analysis of the National Inpatient Sample, 2005–2014. Hospitalizations for surgeries of prostate, bladder, esophagus, pancreas, lung, liver, colorectal, and breast among patients 40 years and greater were included in the analysis. MACCE was defined as a composite measure that included in-hospital all-cause mortality, acute myocardial infarction (AMI), and ischemic stroke. A total of 2,854,810 hospitalizations for major surgeries were included in this study. Of these, 67,316 (2.4%) had perioperative MACCE. Trends of perioperative MACCE showed that it decreased significantly for AMI, death and any MACCE, while stroke did not significantly change during the study period. Logistic regression analysis for perioperative MACCE by cancer types showed that surgeries for esophagus, pancreas, lung, liver, and colorectal cancers had significantly greater odds for perioperative MACCE. The surgeries identified to have greater risks for MACCE in this study could be risk stratified for better informed decision-making and management.

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Introduction: Current estimates show that, globally, there are 531 million cases and 6.3 million deaths due to COVID-19. Studies have shown that COVID-19 could lead to deep vein thrombosis (DVT) resulting in increased morbidity and mortality. In this study we sought to estimate the prevalence of DVT among COVID-19 hospitalizations as well as its effects on hospital outcomes using a large administrative database. Hypothesis: The adverse in-hospital outcomes of COVID-19 will be significantly higher among DVT hospitalizations. Methods: We conducted a retrospective analysis of the 2020 California State Inpatient Database. All hospitalizations with age 18 and above and primary diagnosis of COVID-19 were included for the study. They were classified into those with and without DVT. The main outcomes of the study were in-hospital mortality, prolonged length of stay, vasopressor use, mechanical ventilation, and ICU admission. Length of stay ≥75th percentile was grouped as prolonged length of stay. Multivariate logistic regressions with covariate adjustments were conducted to compare COVID-19 related outcomes between those with and without DVT. Results: We included a total of 94,114 primary COVID-19 hospitalizations for the analysis. Among them 1575 (1.7%) had DVT. The prevalence of mortality (27.5% versus 11.1%, P<0.001), prolonged length of stay (62.2% versus 27.8%, P<0.001), vasopressor use (7.9% versus 2.1%, P<0.001), mechanical ventilation (36.2% versus 9.7%, P<0.001), and ICU admission (35.7% versus 9.3%, P<0.001) were significantly higher among those with DVT. After adjusting for covariates, regression analysis showed that those with DVT had significantly greater odds for mortality (aOR, 2.34, 95% CI: 2.07-2.65), prolonged length of stay (aOR, 3.51, 95% CI: 3.16-3.91), vasopressor use (aOR, 4.23, 95% CI: 3.78-4.74), mechanical ventilation (aOR, 2.90, 95% CI: 2.38-3.53), and ICU admission (aOR, 4.32, 95% CI: 3.85-4.84). Conclusions: In our cohort, only few COVID-19 hospitalizations had a diagnosis of DVT. However, among those with DVT, the risk for adverse outcomes were significantly higher. Since DVT among COVID-19 is uncommon but associated with adverse hospital outcomes, healthcare providers should promptly monitor for DVT and manage it.