ABSTRACT
There is scant information on the clinical progression, end-of-life decisions, and cause of death of patients with cancer diagnosed with COVID-19. Therefore, we conducted a case series of patients admitted to a comprehensive cancer center who did not survive their hospitalization. To determine the cause of death, 3 board-certified intensivists reviewed the electronic medical records. Concordance regarding cause of death was calculated. Discrepancies were resolved through a joint case-by-case review and discussion among the 3 reviewers. During the study period, 551 patients with cancer and COVID-19 were admitted to a dedicated specialty unit; among them, 61 (11.6%) were nonsurvivors. Among nonsurvivors, 31 (51%) patients had hematologic cancers, and 29 (48%) had undergone cancer-directed chemotherapy within 3 months before admission. The median time to death was 15 days (95% confidence interval [CI], 11.8 to 18.2). There were no differences in time to death by cancer category or cancer treatment intent. The majority of decedents (84%) had full code status at admission; however, 53 (87%) had do-not-resuscitate orders at the time of death. Most deaths were deemed to be COVID-19 related (88.5%). The concordance between the reviewers for the cause of death was 78.7%. In contrast to the belief that COVID-19 decedents die because of their comorbidities, in our study only 1 of every 10 patients died of cancer-related causes. Full-scale interventions were offered to all patients irrespective of oncologic treatment intent. However, most decedents in this population preferred care with nonresuscitative measures rather than full support at the end of life.
Subject(s)
COVID-19 , Hematologic Neoplasms , Neoplasms , Humans , Cause of Death , Medical OncologyABSTRACT
OBJECTIVES: Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. METHODS: We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. RESULTS: These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. CONCLUSIONS: Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.
ABSTRACT
INTRODUCTION: Millions of Americans infected with the severe acute respiratory syndrome-associated coronavirus-19 (COVID-19) need oncologic surgery. Patients with acute or resolved COVID-19 illness complain of neuropsychiatric symptoms. How surgery affects postoperative neuropsychiatric outcomes such as delirium is unknown. We hypothesize that patients with a history of COVID-19 could have an exaggerated risk of developing postoperative delirium after undergoing major elective oncologic surgery. METHODS: We conducted a retrospective study to determine the association between COVID-19 status and antipsychotic drugs during postsurgical hospitalization as a surrogate of delirium. Secondary outcomes included 30 days of postoperative complications, length of stay, and mortality. Patients were grouped into pre-pandemic non-COVID-19 and COVID-19-positive groups. A 1:2 propensity score matching was used to minimize bias. A multivariable logistic regression model estimated the effects of important covariates on the use of postoperative psychotic medication. RESULTS: A total of 6003 patients were included in the study. Pre- and post-propensity score matching demonstrated that a history of preoperative COVID-19 did not increase the risk of antipsychotic medications postoperatively. However, respiratory and overall 30-day complications were higher in COVID-19 individuals than in pre-pandemic non-COVID-19 patients. The multivariate analysis showed that the odds of using postoperative antipsychotic medication use for the patients who had COVID-19 compared to those who did not have the infection were not significantly different. CONCLUSION: A preoperative diagnosis of COVID-19 did not increase the risk of postoperative antipsychotic medication use or neurological complications. More studies are needed to reproduce our results due to the increased concern of neurological events post-COVID-19 infection.
ABSTRACT
There is a lack of data focused on the specific coagulopathic derangements in COVID-19 versus non-COVID-19 acutely ill cancer patients. Our objective was to characterize features of coagulopathy in cancer patients with active COVID-19 illness who required hospitalization at MD Anderson in the Texas Medical Center and to correlate those features with thrombotic complications, critical illness, and mortality within the first 30 days after hospital admission for COVID-19 illness. COVID-19 and non-COVID-19 hospitalized cancer patients, with at least five consecutive measures of PT, PTT, d-dimer, and CBC during the same period, were matched 1:1 to perform a retrospective analysis. We reviewed complete blood cell counts with differential, PT, PTT, fibrinogen, D-Dimer, serum ferritin, IL-6, CRP, and peripheral blood smears. Clinical outcomes were thrombosis, mechanical ventilation, critical illness, and death. Compared with matched hospitalized cancer patients without COVID-19, we found elevated neutrophil and lower lymphocyte counts in those with critical illness ( p = 0.00) or death ( p = 0.00); only neutrophils correlated with thrombosis. COVID-19 cancer patients with a platelet count decline during the hospital stay had more frequent critical illness ( p = 0.00) and fatal outcomes ( p = 0.00). Of the inflammatory markers, interleukin-6 showed consistently higher levels in the COVID-19 patients with poor outcomes. The findings of unique platelet changes and coagulopathy during severe COVID-19 illness in the cancer population are of interest to explore disease mechanisms and future risk stratification strategies to help with the management of cancer patients with COVID-19.