Incident Cardiovascular Diseases Among Survivors of High-Risk Stage II-III Colorectal Cancer: A Cluster-Wide Cohort Study.

BACKGROUND: The incidence and survival of colorectal cancer (CRC) are increasing. There is an increasing number of long-term survivors, many of whom are elderly and have comorbidities. We conducted a population-based study in Hong Kong to assess the long-term cardiovascular disease (CVD) incidence associated with adjuvant fluoropyrimidine-based chemotherapy among CRC survivors. PATIENTS AND METHODS: Using the population-based electronic medical database of Hong Kong, we identified adults who were diagnosed with high-risk stage II-III CRC and treated with radical surgery followed by adjuvant fluoropyrimidine-based chemotherapy between 2010 and 2019. We evaluated the cause-specific cumulative incidence of CVD (including ischemic heart disease, heart failure, cardiomyopathy, and stroke) using the flexible parametric competing risk modeling framework. The control group without a history of CVD was selected from among a noncancer random sample from primary care clinics in the same geographic area. RESULTS: We analyzed 1,037 treated patients with CRC and 5,078 noncancer controls. The adjusted cause-specific hazard ratio (HR) for CVD in the cancer cohort compared with the control group was 2.11 (95% CI, 1.39-3.20). The 1-, 5-, and 10-year cause-specific cumulative incidences were 2.0%, 4.5%, and 5.4% in the cancer cohort versus 1.2%, 3.0%, and 3.8% in the control group, respectively. Age at cancer diagnosis (HR per 5-year increase, 1.16; 95% CI, 1.08-1.24), male sex (HR, 1.40; 95% CI, 1.06-1.86), comorbidity (HR, 1.88; 95% CI, 1.36-2.61 for 1 comorbidity vs none, and HR, 6.61; 95% CI, 4.55-9.60 for ≥2 comorbidities vs none), diabetes (HR, 1.38; 95% CI, 1.04-1.84), hypertension (HR, 3.27; 95% CI, 2.39-4.50), and dyslipidemia/hyperlipidemia (HR, 2.53; 95% CI, 1.68-3.81) were associated with incident CVD. CONCLUSIONS: Exposure to adjuvant fluoropyrimidine-based chemotherapy was associated with an increased risk of CVD among survivors of high-risk stage II-III CRC. Cardiovascular risk monitoring of this group throughout cancer survivorship is advisable.

Early-stage predictors of deterioration among 3145 nonsevere SARS-CoV-2-infected people community-isolated in Wuhan, China: A combination of machine learning algorithms and competing risk survival analyses.

OBJECTIVE: To determine which early-stage variables best predicted the deterioration of coronavirus disease 2019 (COVID-19) among community-isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive-to-measure variables. METHODS: Medical records of 3145 people isolated in two Fangcang shelter hospitals (large-scale community isolation centers) from February to March 2020 were accessed. Two complementary methods-machine learning algorithms and competing risk survival analyses-were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID-19. RESULTS: More than a quarter of the 3145 people did not present any symptoms, while one-third ended isolation due to deterioration. Machine learning models identified moderate severity upon admission, old age, and CT ground-glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age ≥ 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery. CONCLUSIONS: Early-stage prediction of COVID-19 deterioration can be made with inexpensive-to-measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self-reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients.

The analysis of COVID-19 in-hospital mortality: A competing risk approach or a cure model?

Competing risk analyses have been widely used for the analysis of in-hospital mortality in which hospital discharge is considered as a competing event. The competing risk model assumes that more than one cause of failure is possible, but there is only one outcome of interest and all others serve as competing events. However, hospital discharge and in-hospital death are two outcomes resulting from the same disease process and patients whose disease conditions were stabilized so that inpatient care was no longer needed were discharged. We therefore propose to use cure models, in which hospital discharge is treated as an observed "cure" of the disease. We consider both the mixture cure model and the promotion time cure model and extend the models to allow cure status to be known for those who were discharged from the hospital. An EM algorithm is developed for the mixture cure model. We also show that the competing risk model, which treats hospital discharge as a competing event, is equivalent to a promotion time cure model. Both cure models were examined in simulation studies and were applied to a recent cohort of COVID-19 in-hospital patients with diabetes. The promotion time model shows that statin use improved the overall survival; the mixture cure model shows that while statin use reduced the in-hospital mortality rate among the susceptible, it improved the cure probability only for older but not younger patients. Both cure models show that treatment was more beneficial among older patients.

Handling death as an intercurrent event in time to recovery analysis in COVID-19 treatment clinical trials.

In clinical trials with the objective to evaluate the treatment effect on time to recovery, such as investigational trials on therapies for COVID-19 hospitalized patients, the patients may face a mortality risk that competes with the opportunity to recover (e.g., be discharged from the hospital). Therefore, an appropriate analytical strategy to account for death is particularly important due to its potential impact on the estimation of the treatment effect. To address this challenge, we conducted a thorough evaluation and comparison of nine survival analysis methods with different strategies to account for death, including standard survival analysis methods with different censoring strategies and competing risk analysis methods. We report results of a comprehensive simulation study that employed design parameters commonly seen in COVID-19 trials and case studies using reconstructed data from a published COVID-19 clinical trial. Our research results demonstrate that, when there is a moderate to large proportion of patients who died before observing their recovery, competing risk analyses and survival analyses with the strategy to censor death at the maximum follow-up timepoint would be able to better detect a treatment effect on recovery than the standard survival analysis that treat death as a non-informative censoring event. The aim of this research is to raise awareness of the importance of handling death appropriately in the time-to-recovery analysis when planning current and future COVID-19 treatment trials.

Factors Predicting Progression to Severe COVID-19: A Competing Risk Survival Analysis of 1753 Patients in Community Isolation in Wuhan, China.

Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19-rather than to recovery-among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients (n = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04-1.58, p = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24-2.01, p < 0.001), and CT signs of ground-glass opacity (HR = 1.39, 95% CI 1.04-1.86, p = 0.024) and infiltrating shadows (HR = 1.84, 95% CI 1.22-2.78, p = 0.004). The risk of progression was found to be lower among patients with nausea or vomiting (HR = 0.53, 95% CI 0.30-0.96, p = 0.036) and headaches (HR = 0.54, 95% CI 0.29-0.99, p = 0.046). Our results suggest that several factors that can be easily measured even in resource-poor settings (dyspnea, sex, and age) can be used to identify mild COVID-19 patients who are at increased risk of disease progression. Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings. Common and unspecific symptoms (headaches, nausea, and vomiting) are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate. Future public health and clinical guidelines should build on this evidence to improve the screening, triage, and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms.

Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness.

BACKGROUND AND OBJECTIVE: Observational studies may provide valuable evidence on real-world causal effects of drug effectiveness in patients with coronavirus disease 2019 (COVID-19). As patients are usually observed from hospital admission to discharge and drug initiation starts during hospitalization, advanced statistical methods are needed to account for time-dependent drug exposure, confounding and competing events. Our objective is to evaluate the observational studies on the three common methodological pitfalls in time-to-event analyses: immortal time bias, confounding bias and competing risk bias. METHODS: We performed a systematic literature search on 23 October 2020, in the PubMed database to identify observational cohort studies that evaluated drug effectiveness in hospitalized patients with COVID-19. We included articles published in four journals: British Medical Journal, New England Journal of Medicine, Journal of the American Medical Association and The Lancet as well as their sub-journals. RESULTS: Overall, out of 255 articles screened, 11 observational cohort studies on treatment effectiveness with drug exposure-outcome associations were evaluated. All studies were susceptible to one or more types of bias in the primary study analysis. Eight studies had a time-dependent treatment. However, the hazard ratios were not adjusted for immortal time in the primary analysis. Even though confounders presented at baseline have been addressed in nine studies, time-varying confounding caused by time-varying treatment exposure and clinical variables was less recognized. Only one out of 11 studies addressed competing event bias by extending follow-up beyond patient discharge. CONCLUSIONS: In the observational cohort studies on drug effectiveness for treatment of COVID-19 published in four high-impact journals, the methodological biases were concerningly common. Appropriate statistical tools are essential to avoid misleading conclusions and to obtain a better understanding of potential treatment effects.

Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them.

By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing in-hospital COVID-19 data.