Overall Survival with Osimertinib in Resected EGFR-Mutated NSCLC.

BACKGROUND: Among patients with resected, epidermal growth factor receptor (EGFR)-mutated, stage IB to IIIA non-small-cell lung cancer (NSCLC), adjuvant osimertinib therapy, with or without previous adjuvant chemotherapy, resulted in significantly longer disease-free survival than placebo in the ADAURA trial. We report the results of the planned final analysis of overall survival. METHODS: In this phase 3, double-blind trial, we randomly assigned eligible patients in a 1:1 ratio to receive osimertinib (80 mg once daily) or placebo until disease recurrence was observed, the trial regimen was completed (3 years), or a discontinuation criterion was met. The primary end point was investigator-assessed disease-free survival among patients with stage II to IIIA disease. Secondary end points included disease-free survival among patients with stage IB to IIIA disease, overall survival, and safety. RESULTS: Of 682 patients who underwent randomization, 339 received osimertinib and 343 received placebo. Among patients with stage II to IIIA disease, the 5-year overall survival was 85% in the osimertinib group and 73% in the placebo group (overall hazard ratio for death, 0.49; 95.03% confidence interval [CI], 0.33 to 0.73; P<0.001). In the overall population (patients with stage IB to IIIA disease), the 5-year overall survival was 88% in the osimertinib group and 78% in the placebo group (overall hazard ratio for death, 0.49; 95.03% CI, 0.34 to 0.70; P<0.001). One new serious adverse event, pneumonia related to coronavirus disease 2019, was reported after the previously published data-cutoff date (the event was not considered by the investigator to be related to the trial regimen, and the patient fully recovered). Adjuvant osimertinib had a safety profile consistent with that in the primary analysis. CONCLUSIONS: Adjuvant osimertinib provided a significant overall survival benefit among patients with completely resected, EGFR-mutated, stage IB to IIIA NSCLC. (Funded by AstraZeneca; ADAURA ClinicalTrials.gov number, NCT02511106.).

Simultaneous hypothesis testing for multiple competing risks in comparative clinical trials.

Competing risks data are commonly encountered in randomized clinical trials or observational studies. Ignoring competing risks in survival analysis leads to biased risk estimates and improper conclusions. Often, one of the competing events is of primary interest and the rest competing events are handled as nuisances. These approaches can be inadequate when multiple competing events have important clinical interpretations and thus of equal interest. For example, in COVID-19 in-patient treatment trials, the outcomes of COVID-19 related hospitalization are either death or discharge from hospital, which have completely different clinical implications and are of equal interest, especially during the pandemic. In this paper we develop nonparametric estimation and simultaneous inferential methods for multiple cumulative incidence functions (CIFs) and corresponding restricted mean times. Based on Monte Carlo simulations and a data analysis of COVID-19 in-patient treatment clinical trial, we demonstrate that the proposed method provides global insights of the treatment effects across multiple endpoints.

Latency function estimation under the mixture cure model when the cure status is available.

This paper addresses the problem of estimating the conditional survival function of the lifetime of the subjects experiencing the event (latency) in the mixture cure model when the cure status information is partially available. The approach of past work relies on the assumption that long-term survivors are unidentifiable because of right censoring. However, in some cases this assumption is invalid since some subjects are known to be cured, e.g., when a medical test ascertains that a disease has entirely disappeared after treatment. We propose a latency estimator that extends the nonparametric estimator studied in López-Cheda et al. (TEST 26(2):353-376, 2017b) to the case when the cure status is partially available. We establish the asymptotic normality distribution of the estimator, and illustrate its performance in a simulation study. Finally, the estimator is applied to a medical dataset to study the length of hospital stay of COVID-19 patients requiring intensive care.

A survival analysis of socio-demographic and clinical predictors among hospitalized COVID-19 patients in Southern Iran.

BACKGROUND: This study aimed to evaluate the socio-demographic, clinical, and laboratory risk factors in hospitalized COVID-19 patients during the first 6 months of the SARS-CoV-2 epidemic. METHOD: This retrospective hospital-based cross-sectional study included all laboratory-confirmed cases of the COVID-19 virus that were admitted to the Shohadaye-Khalije-Fars Hospital in Bushehr, Iran, from February 22, 2020 to September 21, 2020. The patients' records were reviewed during the hospitalization period. The global COVID-19 clinical platform, i.e., the World Health Organization Rapid Case Report Form was used as the data collection tool. We conducted the survival analysis using the Kaplan-Meier and the Stepwise Cox regression analyses. RESULTS: The analysis included 2108 confirmed cases of COVID-19 with a mean age of 47.81 years (SD 17.78); 56.8% men, 43.2% women and 6.3% (n = 133) deaths. After adjustment, it was found that factors associated with an increased risk of death consisted of chronic kidney disease, intensive care unit admission, cancer, and hemoptysis. The 7-day survival rate was 95.8%, which decreased to 95.1%, 94.0%, and 93.8% on days 14, 21, and 28 of hospitalization, respectively. DISCUSSION AND CONCLUSION: Older COVID-19 patients with manifestation of hemoptysis and a past medical history of chronic kidney disease and cancer, should be closely monitored to prevent disease deterioration and death, and also should be admitted to the intensive care unit.

Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods.

Time-to-event data are right-truncated if only individuals who have experienced the event by a certain time can be included in the sample. For example, we may be interested in estimating the distribution of time from onset of disease symptoms to death and only have data on individuals who have died. This may be the case, for example, at the beginning of an epidemic. Right truncation causes the distribution of times to event in the sample to be biased towards shorter times compared to the population distribution, and appropriate statistical methods should be used to account for this bias. This article is a review of such methods, particularly in the context of an infectious disease epidemic, like COVID-19. We consider methods for estimating the marginal time-to-event distribution, and compare their efficiencies. (Non-)identifiability of the distribution is an important issue with right-truncated data, particularly at the beginning of an epidemic, and this is discussed in detail. We also review methods for estimating the effects of covariates on the time to event. An illustration of the application of many of these methods is provided, using data on individuals who had died with coronavirus disease by 5 April 2020.

Deep Survival Analysis With Clinical Variables for COVID-19.

OBJECTIVE: Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients. METHODS AND PROCEDURES: We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups. RESULTS: Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19. CONCLUSION: Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner. CLINICAL IMPACT: The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.

A competing risk survival analysis of the sociodemographic factors of COVID-19 in-hospital mortality in Ecuador.

This study aimed to analyze the effect of sociodemographic characteristics on COVID-19 in-hospital mortality in Ecuador from March 1 to December 31, 2020. This retrospective longitudinal study was performed with data from publicly accessible registries of the Ecuadorian National Institute of Statistics and Censuses (INEC). Data underwent a competing risk analysis with estimates of the cumulative incidence function (CIF). The effect of covariates on CIFs was estimated using the Fine-Gray model and results were expressed as adjusted subdistribution hazard ratios (SHR). The analysis included 30,991 confirmed COVID-19 patients with a mean age of 56.57±18.53 years; 60.7% (n = 18,816) were men and 39.3% (n = 12,175) were women. Being of advanced age, especially older than or equal to 75 years (SHR = 17.97; 95%CI: 13.08-24.69), being a man (SHR = 1.29; 95%CI: 1.22-1.36), living in rural areas (SHR = 1.18; 95%CI: 1.10-1.26), and receiving care in a public health center (SHR = 1.64; 95%CI: 1.51-1.78) were factors that increased the incidence of death from COVID-19, while living at an elevation higher than 2,500 meters above sea level (SHR = 0.69; 95%CI: 0.66-0.73) decreased this incidence. Since the incidence of death for individuals living in rural areas and who received medical care from the public sector was higher, income and poverty are important factors in the final outcome of this disease.

An Expectation-Maximization Algorithm for Including Oncological COVID-19 Deaths in Survival Analysis.

We address the problem of how COVID-19 deaths observed in an oncology clinical trial can be consistently taken into account in typical survival estimates. We refer to oncological patients since there is empirical evidence of strong correlation between COVID-19 and cancer deaths, which implies that COVID-19 deaths cannot be treated simply as non-informative censoring, a property usually required by the classical survival estimators. We consider the problem in the framework of the widely used Kaplan-Meier (KM) estimator. Through a counterfactual approach, an algorithmic method is developed allowing to include COVID-19 deaths in the observed data by mean-imputation. The procedure can be seen in the class of the Expectation-Maximization (EM) algorithms and will be referred to as Covid-Death Mean-Imputation (CoDMI) algorithm. We discuss the CoDMI underlying assumptions and the convergence issue. The algorithm provides a completed lifetime data set, where each Covid-death time is replaced by a point estimate of the corresponding virtual lifetime. This complete data set is naturally equipped with the corresponding KM survival function estimate and all available statistical tools can be applied to these data. However, mean-imputation requires an increased variance of the estimates. We then propose a natural extension of the classical Greenwood's formula, thus obtaining expanded confidence intervals for the survival function estimate. To illustrate how the algorithm works, CoDMI is applied to real medical data extended by the addition of artificial Covid-death observations. The results are compared with the estimates provided by the two naïve approaches which count COVID-19 deaths as censoring or as deaths by the disease under study. In order to evaluate the predictive performances of CoDMI an extensive simulation study is carried out. The results indicate that in the simulated scenarios CoDMI is roughly unbiased and outperforms the estimates obtained by the naïve approaches. A user-friendly version of CoDMI programmed in R is freely available.

High flow nasal cannula for acute respiratory failure due to COVID-19 in patients with a 'do-not-intubate' order: A survival analysis.

INTRODUCTION: High flow nasal cannula (HFNC) reduces the need for intubation in patients with hypoxaemic acute respiratory failure (ARF), but its added value in patients with severe coronavirus disease 2019 (COVID-19) and a do-not-intubate (DNI) order is unknown. We aimed to assess (variables associated with) survival in these patients. MATERIALS AND METHODS: We described a multicentre retrospective observational cohort study in five hospitals in the Netherlands and assessed the survival in COVID-19 patients with severe acute respiratory failure and a DNI order who were treated with high flow nasal cannula. We also studied variables associated with survival. RESULTS AND DISCUSSION: One-third of patients survived after 30 days. Survival was 43.9% in the subgroup of patients with a good WHO performance status and only 16.1% in patients with a poor WHO performance status. Patients who were admitted to the hospital for a longer period prior to HFNC initiation were less likely to survive. HFNC resulted in an increase in ROX values, reflective of improved oxygenation and/or decreased respiratory rate. CONCLUSION: Our data suggest that a trial of HFNC could be considered to increase chances of survival in patients with ARF due to COVID-19 pneumonitis and a DNI order, especially in those with a good WHO performance status.

Survival analysis and mortality predictors of COVID-19 in a pediatric cohort in Mexico.

Background: The new coronavirus SARS-CoV-2 pandemic has been relatively less lethal in children; however, poor prognosis and mortality has been associated with factors such as access to health services. Mexico remained on the list of the ten countries with the highest case fatality rate (CFR) in adults. It is of interest to know the behavior of COVID-19 in the pediatric population. The aim of this study was to identify clinical and sociodemographic variables associated with mortality due to COVID-19 in pediatric patients. Objective: Using National open data and information from the Ministry of Health, Mexico, this cohort study aimed to identify clinical and sociodemographic variables associated with COVID-19 mortality in pediatric patients. Method: A cohort study was designed based on National open data from the Ministry of Health, Mexico, for the period April 2020 to January 2022, and included patients under 18 years of age with confirmed SARS-CoV-2 infection. Variables analyzed were age, health services used, and comorbidities (obesity, diabetes, asthma, cardiovascular disease, immunosuppression, high blood pressure, and chronic kidney disease). Follow-up duration was 60 days, and primary outcomes were death, hospitalization, and requirement of intensive care. Statistical analysis included survival analysis, prediction models created using the Cox proportional hazards model, and Kaplan-Meier estimation curves. Results: The cohort included 261,099 cases with a mean age of 11.2 ± 4 years, and of these, 11,569 (4.43%) were hospitalized and 1,028 (0.39%) died. Variables associated with risk of mortality were age under 12 months, the presence of comorbidities, health sector where they were treated, and first wave of infection. Conclusion: Based on data in the National database, we show that the pediatric fatality rate due to SARS-CoV-2 is similar to that seen in other countries. Access to health services and distribution of mortality were heterogeneous. Vulnerable groups were patients younger than 12 months and those with comorbidities.

Survival Analysis Using the Covid-death Mean-imputation (CoDMI) Algorithm: A First Clinical Application in Radiation Oncology.

BACKGROUND/AIM: To report long-term survival results after trimodal approach for locally advanced rectal cancer (LARC) in the Covid-19 era. We herein illustrate a clinical application of Covid-death mean-imputation (CoDMI) algorithm in LARC patients with Covid-19 infection. PATIENTS AND METHODS: We analyzed 94 patients treated for primary LARC. Overall survival was calculated in months from diagnosis to first event (last follow-up/death). Because Covid-19 death events potentially bias survival estimation, to eliminate skewed data due to Covid-19 death events, the observed lifetime of Covid-19 cases was replaced by its corresponding expected lifetime in absence of the Covid-19 event using the CoDMI algorithm. Patients who died of Covid-19 (DoC) are mean-imputed by the Kaplan-Meier estimator. Under this approach, the observed lifetime of each DoC patient is considered as an "incomplete data" and is extended by an additional expected lifetime computed using the classical Kaplan-Meier model. RESULTS: Sixteen patients were dead of disease (DoD), 1 patient was DoC and 77 cases were censored (Cen). The DoC patient died of Covid-19 52 months after diagnosis. The CoDMI algorithm computed the expected future lifetime provided by the Kaplan-Meier estimator applied to the no-DoC observations as well as to the DoC data itself. Given the DoC event at 52 months, the CoDMI algorithm estimated that this patient would have died after 79.5 months of follow-up. CONCLUSION: The CoDMI algorithm leads to "unbiased" probability of overall survival in LARC patients with Covid-19 infection, compared to that provided by a naïve application of Kaplan-Meier approach. This allows for a proper interpretation/use of Covid-19 events in survival analysis. A user-friendly version of CoDMI is freely available at https://github.com/alef-innovation/codmi.

Time from symptom onset to severe COVID-19 and risk factors among patients in Southern Ethiopia: a survival analysis.

OBJECTIVE: This study aimed to assess the time to severe coronavirus disease 2019 (COVID-19) and risk factors among confirmed COVID-19 cases in Southern Ethiopia. METHOD: This two-center retrospective cohort study involved patients with confirmed COVID-19 from 1 October 2020 to 30 September 2021. Kaplan-Meier graphs and log-rank tests were used to determine the pattern of COVID-19 severity among categories of variables. Bivariable and multivariable Cox proportional regression models were used to identify the risk factors of severe COVID-19. RESULTS: Four hundred thirteen patients with COVID-19 with a mean age of 41.9 ± 15.3 years were involved in the study. There were 194 severe cases (46.9.1%), including 77 (39.6%) deaths. The median time from symptom onset to severe COVID-19 was 8 days (interquartile range: 7-12 days). The risk factors for severe COVID-19 were age >65 (adjusted hazard ratio [AHR] = 2.65, 95% confidence interval [95%CI]: 1.02, 3.72), cough (AHR = 1.59, 95%CI: 1.39, 2.84), chest pain (AHR = 1.47, 95%CI: 1.34, 2.66), headache (AHR = 2.04, 95%CI: 1.43, 2.88), comorbidity (AHR = 1.3, 95%CI: 1.01, 2.04), asthma (AHR = 1.6. 95%CI: 1.04, 2.24), and symptom onset to admission more than 5 days (AHR = 0.48, 95%CI: 0.34, 0.68). CONCLUSION: Patients with symptoms and comorbidities should be closely monitored.

Ibrutinib plus Bendamustine and Rituximab in Untreated Mantle-Cell Lymphoma.

BACKGROUND: Ibrutinib, a Bruton's tyrosine kinase inhibitor, may have clinical benefit when administered in combination with bendamustine and rituximab and followed by rituximab maintenance therapy in older patients with untreated mantle-cell lymphoma. METHODS: We randomly assigned patients 65 years of age or older to receive ibrutinib (560 mg, administered orally once daily until disease progression or unacceptable toxic effects) or placebo, plus six cycles of bendamustine (90 mg per square meter of body-surface area) and rituximab (375 mg per square meter). Patients with an objective response (complete or partial response) received rituximab maintenance therapy, administered every 8 weeks for up to 12 additional doses. The primary end point was progression-free survival as assessed by the investigators. Overall survival and safety were also assessed. RESULTS: Among 523 patients, 261 were randomly assigned to receive ibrutinib and 262 to receive placebo. At a median follow-up of 84.7 months, the median progression-free survival was 80.6 months in the ibrutinib group and 52.9 months in the placebo group (hazard ratio for disease progression or death, 0.75; 95% confidence interval, 0.59 to 0.96; P = 0.01). The percentage of patients with a complete response was 65.5% in the ibrutinib group and 57.6% in the placebo group (P = 0.06). Overall survival was similar in the two groups. The incidence of grade 3 or 4 adverse events during treatment was 81.5% in the ibrutinib group and 77.3% in the placebo group. CONCLUSIONS: Ibrutinib treatment in combination with standard chemoimmunotherapy significantly prolonged progression-free survival. The safety profile of the combined therapy was consistent with the known profiles of the individual drugs. (Funded by Janssen Research and Development and Pharmacyclics; SHINE ClinicalTrials.gov number, NCT01776840.).

Nonparametric kernel estimation of the probability of cure in a mixture cure model when the cure status is partially observed.

Cure models are a class of time-to-event models where a proportion of individuals will never experience the event of interest. The lifetimes of these so-called cured individuals are always censored. It is usually assumed that one never knows which censored observation is cured and which is uncured, so the cure status is unknown for censored times. In this paper, we develop a method to estimate the probability of cure in the mixture cure model when some censored individuals are known to be cured. A cure probability estimator that incorporates the cure status information is introduced. This estimator is shown to be strongly consistent and asymptotically normally distributed. Two alternative estimators are also presented. The first one considers a competing risks approach with two types of competing events, the event of interest and the cure. The second alternative estimator is based on the fact that the probability of cure can be written as the conditional mean of the cure status. Hence, nonparametric regression methods can be applied to estimate this conditional mean. However, the cure status remains unknown for some censored individuals. Consequently, the application of regression methods in this context requires handling missing data in the response variable (cure status). Simulations are performed to evaluate the finite sample performance of the estimators, and we apply them to the analysis of two datasets related to survival of breast cancer patients and length of hospital stay of COVID-19 patients requiring intensive care.

Key characteristics impacting survival of COVID-19 extracorporeal membrane oxygenation.

BACKGROUND: Severe COVID-19 induced acute respiratory distress syndrome (ARDS) often requires extracorporeal membrane oxygenation (ECMO). Recent German health insurance data revealed low ICU survival rates. Patient characteristics and experience of the ECMO center may determine intensive care unit (ICU) survival. The current study aimed to identify factors affecting ICU survival of COVID-19 ECMO patients. METHODS: 673 COVID-19 ARDS ECMO patients treated in 26 centers between January 1st 2020 and March 22nd 2021 were included. Data on clinical characteristics, adjunct therapies, complications, and outcome were documented. Block wise logistic regression analysis was applied to identify variables associated with ICU-survival. RESULTS: Most patients were between 50 and 70 years of age. PaO2/FiO2 ratio prior to ECMO was 72 mmHg (IQR: 58-99). ICU survival was 31.4%. Survival was significantly lower during the 2nd wave of the COVID-19 pandemic. A subgroup of 284 (42%) patients fulfilling modified EOLIA criteria had a higher survival (38%) (p = 0.0014, OR 0.64 (CI 0.41-0.99)). Survival differed between low, intermediate, and high-volume centers with 20%, 30%, and 38%, respectively (p = 0.0024). Treatment in high volume centers resulted in an odds ratio of 0.55 (CI 0.28-1.02) compared to low volume centers. Additional factors associated with survival were younger age, shorter time between intubation and ECMO initiation, BMI > 35 (compared to < 25), absence of renal replacement therapy or major bleeding/thromboembolic events. CONCLUSIONS: Structural and patient-related factors, including age, comorbidities and ECMO case volume, determined the survival of COVID-19 ECMO. These factors combined with a more liberal ECMO indication during the 2nd wave may explain the reasonably overall low survival rate. Careful selection of patients and treatment in high volume ECMO centers was associated with higher odds of ICU survival. TRIAL REGISTRATION: Registered in the German Clinical Trials Register (study ID: DRKS00022964, retrospectively registered, September 7th 2020, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00022964 .

Characteristics of mechanically ventilated COVID-19 patients in the Al-Ahsa Region of Saudi Arabia: a retrospective study with survival analysis.

BACKGROUND: About 5-10% of coronavirus disease 2019 (COVID-19) infected patients require critical care hospitalization and a variety of respiratory support, including invasive mechanical ventilation. Several nationwide studies from Saudi Arabia have identified common comorbidities but none were focused on mechanically ventilated patients in the Al-Ahsa region of Saudi Arabia. OBJECTIVES: Identify characteristics and risk factors for mortality in mechanically ventilated COVID-19 patients. DESIGN: Retrospective chart review SETTING: Two general hospitals in the Al-Ahsa region of Saudi Arabia PATIENTS AND METHODS: We included mechanically ventilated COVID-19 patients (>18 years old) admitted between 1 May and 30 November 2020, in two major general hospitals in the Al-Ahsa region, Saudi Arabia. Descriptive statistics were used to characterize patients. A multivariable Cox proportional hazards (CPH) model was used exploratively to identify hazard ratios (HR) of predictors of mortality. MAIN OUTCOME MEASURES: Patient characteristics, mortality rate, extubation rate, the need for re-intubation and clinical complications during hospitalization. SAMPLE SIZE AND CHARACTERISTICS: 154 mechanically ventilated COVID-19 patients with median (interquartile range) age of 60 (22) years; 65.6% male. RESULTS: Common comorbidities were diabetes (72.2%), hypertension (67%), cardiovascular disease (14.9%) and chronic kidney disease (CKD) (14.3%). In the multivariable CPH model, age >60 years old (HR=1.83, 95% CI 1.2-2.7, P=.002), CKD (1.61, 95% CI 0.9-2.6, P=.062), insulin use (HR=0.65, 95% CI 0.35-.08, P<.001), and use of loop diuretics (HR=0.51, 95% CI 0.4, P=.037) were major predictors of mortality. CONCLUSION: Common diseases in mechanically ventilated COVID-19 patients from the Al-Ahsa region were diabetes, hypertension, other cardiovascular diseases, and CKD in this exploratory analysis. LIMITATIONS: Retrospective, weak CPH model performance. CONFLICTS OF INTEREST: None.

Dynamic survival analysis for non-Markovian epidemic models.

We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.