A population-based study of COVID-19 mortality risk in US cancer patients.

BACKGROUND: In this study, we provide the largest analysis to date of a US-based cancer cohort to characterize death from COVID-19. METHODS: A total of 4,020,669 patients across 15 subtypes living with cancer in 2020 and included in the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database were abstracted. We investigated prognostic factors for death due to COVID-19 using a cox proportional hazards model and calculated hazard ratios (HRs). Standardized mortality ratios (SMRs) were calculated using observed mortality counts from SEER and expected mortality based on U.S. mortality rates. RESULTS: 291,323 patients died, with 14,821 (5.1%) deaths attributed to COVID-19 infection. The COVID-19 disease-specific mortality rate was 11.81/10,000-persons years, and SMR of COVID-19 was 2.30 (95% CI: 2.26-2.34, p < .0001). COVID-19 ranked as the second leading cause of death following ischemic heart disease (5.2%) among 26 non-cancer causes of death. Patients who are older (80+ vs < =49 years old: HR 21.47, 95% CI: 19.34-23.83), male (vs female: HR 1.46, 95% CI: 1.40-1.51), unmarried (vs married: HR 1.47, 95% CI: 1.42-1.53), and Hispanic or Non-Hispanic African American (vs Non-Hispanic White: HR 2.04, 95% CI: 1.94-2.14 and HR 2.03, 95% CI: 1.94-2.14, respectively) were at greatest risk of COVID-19 mortality. CONCLUSIONS AND RELEVANCE: We observed that people living with cancer are at two times greater risk of dying from COVID-19 compared to the general US population. This work may be used by physicians and public health officials in the creation of survivorship programs that mitigate the risk of COVID-19 mortality.

Causes of death among people living with metastatic cancer.

Studying survivorship and causes of death in patients with advanced or metastatic cancer remains an important task. We characterize the causes of death among patients with metastatic cancer, across 13 cancer types and 25 non-cancer causes and predict the risk of death after diagnosis from the diagnosed cancer versus other causes (e.g., stroke, heart disease, etc.). Among 1,030,937 US (1992-2019) metastatic cancer survivors, 82.6% of patients (n = 688,529) died due to the diagnosed cancer, while 17.4% (n = 145,006) died of competing causes. Patients with lung, pancreas, esophagus, and stomach tumors are the most likely to die of their metastatic cancer, while those with prostate and breast cancer have the lowest likelihood. The median survival time among patients living with metastases is 10 months; our Fine and Gray competing risk model predicts 1 year survival with area under the receiver operating characteristic curve of 0.754 (95% CI [0.754, 0.754]). Leading non-cancer deaths are heart disease (32.4%), chronic obstructive and pulmonary disease (7.9%), cerebrovascular disease (6.1%), and infection (4.1%).

Association of Socioeconomic Status With Worse Overall Survival in Patients With Bone and Joint Cancer.

BACKGROUND: The effect of socioeconomic status (SES) on the outcomes of patients with metastatic cancer to bone has not been adequately studied. We analyzed the association between the Yost Index, a composite geocoded SES score, and overall survival among patients who underwent nonprimary surgical resection for bone metastases. METHODS: This population-based study used data from the National Cancer Institute's Surveillance, Epidemiology, and End Results database (2010 to 2018). We categorized bone and joint sites using International Classification of Disease-O-3 recodes. The Yost Index was geocoded using a factor analysis and categorized into quintiles using census tract-level American Community Service 5-year estimates and seven measures: median household income, median house value, median rent, percent below 150% of the poverty line, education index, percent working class, and percent unemployed. Multivariate Cox regression models were used to calculate adjusted hazard ratios of overall survival and 95% confidence intervals. RESULTS: A total of 138,158 patients were included. Patients with the lowest SES had 34% higher risk of mortality compared with those with the highest SES (adjusted hazard ratio of 1.34, 95% confidence interval: 1.32 to 1.37, P < 0.001). Among patients who underwent nonprimary surgery of the distant bone tumor (n = 11,984), the age-adjusted mortality rate was 31.3% higher in the lowest SES patients compared with the highest SES patients (9.9 versus 6.8 per 100,000, P < 0.001). Patients in the lowest SES group showed more racial heterogeneity (63.0% White, 33.5% Black, 3.1% AAPI) compared with the highest SES group (83.9% White, 4.0% Black, 11.8% AAPI, P < 0.001). Higher SES patients are more likely to be married (77.5% versus 59.0%, P < 0.0001) and to live in metropolitan areas (99.6% versus 73.6%, P < 0.0001) compared with lower SES patients. DISCUSSION: Our results may have implications for developing interventions to improve access and quality of care for patients from lower SES backgrounds, ultimately reducing disparities in orthopaedic surgery.

Reirradiation with stereotactic body radiotherapy for primary or secondary lung malignancies: Tumor control probability and safety analyses.

BACKGROUND: Reirradiation with stereotactic body radiotherapy (SBRT) for patients with primary or secondary lung malignancies represents an appealing definitive approach, but its feasibility and safety are not well defined. The purpose of this study was to investigate the tumor control probability (TCP) and toxicity for patients receiving reirradiation with SBRT. PATIENTS AND METHODS: Eligible patients with recurrence of primary or secondary lung malignancies from our hospital were subjected to reirradiation with SBRT, and PubMed- and Embase-indexed articles were reviewed. The patient characteristics, pertinent SBRT dosimetric details, local tumor control, and toxicities were extracted. The logistic dose-response models were compared for TCP and overall survival (OS) in terms of the physical dose and three-, four-, and five-fraction equivalent doses. RESULTS: The data of 17 patients from our hospital and 195 patients extracted from 12 articles were summarized. Reirradiation with SBRT yielded 2-year estimates of 80% TCP for doses of 50.10 Gy, 55.85 Gy, and 60.54 Gy in three, four, and five fractions, respectively. The estimated TCP with common fractionation schemes were 50%, 60%, and 70% for 42.04 Gy, 47.44 Gy, and 53.32 Gy in five fractions, respectively. Similarly, the 2-year estimated OS was 50%, 60%, and 70% for 41.62 Gy, 46.88 Gy, and 52.55 Gy in five fractions, respectively. Central tumor localization may be associated with severe toxicity. CONCLUSIONS: Reirradiation with SBRT doses of 50-60 Gy in 3-5 fractions is feasible for appropriately selected patients with recurrence of peripheral primary or secondary lung malignancies, but should be carefully considered for centrally-located tumors due to potentially severe toxicity. Further studies are warranted for optimal dose/fractionation schedules and more accurate selection of patients suitable for reirradiation with SBRT.

Future trends in incidence and long-term survival of metastatic cancer in the United States.

BACKGROUND: Previous studies have demonstrated epidemiological trends in individual metastatic cancer subtypes; however, research forecasting long-term incidence trends and projected survivorship of metastatic cancers is lacking. We assess the burden of metastatic cancer to 2040 by (1) characterizing past, current, and forecasted incidence trends, and (2) estimating odds of long-term (5-year) survivorship. METHODS: This retrospective, serial cross-sectional, population-based study used registry data from the Surveillance, Epidemiology, and End Results (SEER 9) database. Average annual percentage change (AAPC) was calculated to describe cancer incidence trends from 1988 to 2018. Autoregressive integrating moving average (ARIMA) models were used to forecast the distribution of primary metastatic cancer and metastatic cancer to specific sites from 2019 to 2040 and JoinPoint models were fitted to estimate mean projected annual percentage change (APC). RESULTS: The average annual percent change (AAPC) in incidence of metastatic cancer decreased by 0.80 per 100,000 individuals (1988-2018) and we forecast an APC decrease by 0.70 per 100,000 individuals (2018-2040). Analyses predict a decrease in metastases to liver (APC = -3.40, 95% CI [-3.50, -3.30]), lung (APC (2019-2030) = -1.90, 95% CI [-2.90, -1.00]); (2030-2040) = -3.70, 95% CI [-4.60, -2.80]), bone (APC = -4.00, 95% CI [-4.30, -3.70]), and brain (APC = -2.30, 95% CI [-2.60, -2.00]). By 2040, patients with metastatic cancer are predicted to have 46.7% greater odds of long-term survivorship, driven by increasing plurality of patients with more indolent forms of metastatic disease. CONCLUSIONS: By 2040, the distribution of metastatic cancer patients is predicted to shift in predominance from invariably fatal to indolent cancers subtypes. Continued research on metastatic cancers is important to guide health policy and clinical intervention efforts, and direct allocations of healthcare resources.

Cancer that has spread beyond the area where it originated and into different organs is called metastatic cancer. This study analyzed trends in metastatic cancer incidence, the proportion of those with metastatic cancer surviving 5 years after diagnosis and the locations in the body each cancer had spread to. The incidence of metastatic cancer decreased between 1988 and 2018 and is expected to continue to decrease until 2040. Some of the most common locations cancer spreads to is the lung, liver, brain, and bone. Metastatic cancer incidence to these areas is predicted to decrease. Also, the likelihood of surviving for more than 5 years after diagnosis with metastatic cancer is predicted to increase by 2040. This research should facilitate optimal planning of future healthcare resources and policy.

Unknown Causes of Death in Cancer Patients.

OBJECTIVES: Deaths from an unknown cause are difficult to adjudicate and oncologic studies of comparative effectiveness often demonstrate inconsistencies in incorporating these deaths and competing events (eg, heart disease and stroke) in their analyses. In this study, we identify cancer patients most at risk for death of an unknown cause. METHODS: This retrospective, population-based study used cancer registry data from the Surveillance, Epidemiology, and End Results database (1992-2015). The absolute rate of unknown causes of death (COD) cases stratified by sex, marital status, race, treatment, and cancer site were calculated and a multivariable logistic regression model was applied to obtain adjusted odds ratios with 95% CIs. RESULTS: Out of 7,154,779 cancer patients across 22 cancer subtypes extracted from Surveillance, Epidemiology, and End Results, 3,448,927 died during follow-up and 276,068 (7.4%) of these deaths were from unknown causes. Patients with an unknown COD had a shorter mean survival time compared with patients with known COD (36.3 vs 65.7 mo, P < 0.001). The contribution of unknown COD to total mortality was highest in patients with more indolent cancers (eg, prostate [12.7%], thyroid [12.3%], breast [10.7%]) and longer follow-up (eg, >5 to 10 y). One, 3, and 5-year cancer-specific survival (CSS) calculations including unknown COD were significantly decreased compared with CSS estimates excluding cancer patients with unknown COD. CONCLUSION: Of the patients, 7.4% died of unknown causes during follow-up and the proportion of death was higher with longer follow-up and among more indolent cancers. The attribution of high percentages of unknown COD to cancer or non-cancer causes could impact population-based cancer registry studies or clinical trial outcomes with respect to measures involving CSS and mortality.

Cyclic electron flow around photosystem II in silico: How it works and functions in vivo.

To date, cyclic electron flow around PSI (PSI-CEF) has been considered the primary (if not the only) mechanism accepted to adjust the ratio of linear vs cyclic electron flow that is essential to adjust the ratio of ATP/NADPH production needed for CO2 carboxylation. Here we provide a kinetic model showing that cyclic electron flow within PSII (PSII-CEF) is essential to account for the accelerating rate of decay in flash-induced oscillations of O2 yield as the PQ pool progressively reduces to PQH2. Previously, PSII-CEF was modeled by backward transitions using empirical Markov models like Joliot-Kok (J-K) type. Here, we adapted an ordinary differential equation methodology denoted RODE1 to identify which microstates within PSII are responsible for branching between PSII-CEF and Linear Electron Flow (LEF). We applied it to simulate the oscillations of O2 yield from both Chlorella ohadii, an alga that shows strong PSII-CEF attributed to high backward transitions, and Synechococcus elongatus sp. 7002, a widely studied model cyanobacterium. RODE2 simulations reveal that backward transitions occur in microstates that possess a QB- semiquinone prior to the flash. Following a flash that forms microstates populating (QAQB)2-, PSII-CEF redirects these two electrons to the donor side of PSII only when in the oxidized S2 and S3 states. We show that this backward transition pathway is the origin of the observed period-2 oscillations of flash O2 yield and contributes to the accelerated decay of period-4 oscillations. This newly added pathway improved RODE1 fits for cells of both S. elongatus and C. ohadii. RODE2 simulations show that cellular adaptation to high light intensity growth is due to a decrease in QB availability (empty or blocked by Q2-B), or equivalently due to a decrease in the difference in reduction potential relative to QA/QA-. PSII-CEF provides an alternative mechanism for rebalancing the NADPH:ATP ratio that occurs rapidly by adjusting the redox level of the PQ:PQH2 pool and is a necessary process for energy metabolism in aquatic phototrophs.