Accelerated Failure Time Survival Research Articles

An Empirical Estimation of NCAA Head Football Coaches Contract Duration

Abstract While previous research on head coach dismissals finds that team performance and managerial characteristics impact employment survival, these studies ignore the impact of contract modifications. Using an accelerated failure time survival model with hand-collected college head football coach contract data, I find that race/ethnicity affects the hazard rate of contract modifications but not the hazard rate of a head coach’s dismissal. Additionally, analysis of employment duration ignoring contract modifications may result in erroneous conclusions with respect to the effect that race/ethnicity has on a head coach’s dismissal.

Modelling long-term outcomes for patients with systemic lupus erythematosus

BackgroundNew treatments for systemic lupus erythematosus (SLE) aim to improve tolerability and disease activity control over standard of care (SoC) treatment. SoC typically includes daily glucocorticoid (GC) which carries a risk of organ damage over time. This study sought to develop natural history models to identify predictors of long-term outcomes with current SoC SLE treatment. MethodsGeneralized linear and parametric accelerated failure time survival models (GLM) and parametric accelerated failure time (AFT) survival models were designed to identify predictors of disease activity, flare rate, GC use, organ damage, and mortality beyond the first year of treatment in patients with SLE. Models were run using a longitudinal retrospective analysis of prospectively collected Toronto Lupus Cohort (TLC) study data, collected between 1997 and 2020. Covariates of clinical and statistical significance were selected by bivariate- then multi-variate regression to find the model of best fit. FindingsOf the 1255 subjects included, 89 % were female 89 % and 65 % Caucasian. Mean follow-up was 10·5 years. At first visit, 51 % of patients had moderate-to-severe disease activity (SLEDAI-2 K score ≥ 6). Mean organ damage scores gradually increased over the years following diagnosis. Median survival of the cohort was ∼35 years from study entry. In the GLM models, SLEDAI-2 K yearly average, and average GC dose were key for predicting change in SLEDAI-2 K, GC use/ dose, and flare (any/rate). Together, adjusted mean SLEDAI-2 K and GC dose were shown to be predictors of mortality and damage in at least 9 of 12 organ systems considered. InterpretationThese comprehensive, longitudinal, predictive models show that disease activity and GC use are significant predictors of organ damage and mortality in a patient population with predominantly moderate to severe SLE. This deepens understanding of SLE natural history and underscores the need for new treatment approaches that reduce disease activity and GC use with an aim to improve long-term SLE outcomes. FundingThis study was funded by AstraZeneca.

Measuring the Health Co-Benefits of Air Pollution Interventions on Premature Deaths in Canadian Cities

Background: Climate change has significant consequences on human health. Cities are especially vulnerable, where air pollution is a major environmental health risk. Premature mortality (i.e., deaths before age 75) is a robust population health outcome amenable to targeted policy and programmatic interventions. We used the Premature Mortality Population Risk Tool augmented with environmental data (PreMPoRT-ENV) to predict the 5-year incidence of premature deaths under air pollution reduction policies. Methods: PreMPoRT-ENV is a sex-specific Weibull accelerated failure time survival model that uses the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Death Database and environmental data. We applied PreMPoRT-ENV to the 2016–2017 CCHS cycles and simulated Canadian Ambient Air Quality Standards targets to predict their impact on premature mortality across Canadian census metropolitan areas. We simulated capping annual mean particulate matter 2.5 microns or less in diameter (PM2.5) and nitrogen dioxide (NO2), as well as reducing air pollutants by 10% and 25% plus capping. Results: The weighted sample included 9,240,000 females and 9,260,000 males. Capping PM2.5 to 8.8 μg/m3 and NO2 to 12.0 ppb resulted in 12 per 100,000 fewer predicted premature deaths than observed exposures over 5 years (1,110 fewer absolute premature deaths). Reducing air pollutants by 10% and 25% plus capping resulted in even fewer predicted premature deaths. Conclusion: Our study highlights how to use a model that predicts premature mortality to provide estimates of the health impacts of environmental vulnerabilities. Results suggest that more aggressive targets may be needed to further realize population health benefits.

Variation in Breastfeeding Initiation and Duration by Mode of Childbirth: A Prospective, Population-Based Study.

Introduction: Despite known benefits of breastfeeding, including prevention against infections for infants, in the presence of numerous barriers, less than half of infants in high-income countries breastfeed for 6 months. One potential barrier to breastfeeding is birth by cesarean section (C-Section), which can invoke long-term difficulties. However, our structured literature review found that existing empirical research does not fully elucidate this relationship due to differences in operationalization of C-section and breastfeeding, omission of important confounders, and failure to exclude those who did not initiate breastfeeding (or use time-to-event analyses). In this article, we attempt to overcome these limitations. Methods: We analyzed data from 14,414 mother-infant dyads enrolled in the United Kingdom-based prospective Millennium Cohort Study, beginning in 2001. Using multivariable logistic regression, we examined the association between mode of birth (vaginal, emergency C-section, and elective C-section) and likelihood of breastfeeding initiation. We then applied adjusted Accelerated Failure Time survival models to examine the associations between mode of birth and duration of any and exclusive breastfeeding. Results: Those with planned (but not emergency) C-section were less likely to initiate breastfeeding (odds ratio: 0.84, 95% confidence interval [CI]: 0.71-0.99) relative to vaginal births. However, those with either planned or unplanned C-section discontinued both any and exclusive breastfeeding sooner than vaginal births. This effect was more pronounced for those with planned C-section (time ratio [TR]: 0.75, 95% CI: 0.64-0.89) than unplanned C-section (TR: 0.85, 95% CI: 0.74, 0.97) compared with vaginal births. Conclusions: Through application of rigorous methods, this study provides compelling evidence that breastfeeding duration may be impeded by C-section birth. The findings suggest that additional support for mothers who intend to breastfeed and have a C-section birth may be warranted.

A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT)

IntroductionAvoidable hospitalizations are considered preventable given effective and timely primary care management and are an important indicator of health system performance. The ability to predict avoidable hospitalizations at the population level represents a significant advantage for health system decision-makers that could facilitate proactive intervention for ambulatory care-sensitive conditions (ACSCs). The aim of this study is to develop and validate the Avoidable Hospitalization Population Risk Tool (AvHPoRT) that will predict the 5-year risk of first avoidable hospitalization for seven ACSCs using self-reported, routinely collected population health survey data.Methods and analysisThe derivation cohort will consist of respondents to the first 3 cycles (2000/01, 2003/04, 2005/06) of the Canadian Community Health Survey (CCHS) who are 18–74 years of age at survey administration and a hold-out data set will be used for external validation. Outcome information on avoidable hospitalizations for 5 years following the CCHS interview will be assessed through data linkage to the Discharge Database (1999/2000–2017/2018) for an estimated sample size of 394,600. Candidate predictor variables will include demographic characteristics, socioeconomic status, self-perceived health measures, health behaviors, chronic conditions, and area-based measures. Sex-specific algorithms will be developed using Weibull accelerated failure time survival models. The model will be validated both using split set cross-validation and external temporal validation split using cycles 2000–2006 compared to 2007–2012. We will assess measures of overall predictive performance (Nagelkerke R2), calibration (calibration plots), and discrimination (Harrell’s concordance statistic). Development of the model will be informed by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement.Ethics and disseminationThis study was approved by the University of Toronto Research Ethics Board. The predictive algorithm and findings from this work will be disseminated at scientific meetings and in peer-reviewed publications.

A prospective study on the relationship between polycystic ovary syndrome and age at natural menopause.

This study aimed to determine whether polycystic ovary syndrome (PCOS) was associated with age at menopause, compared with women without PCOS, after adjusting for potential confounders. A total of 1,696 reproductive-aged participants from the Tehran Lipid and Glucose Study were included in this population-based prospective study with a follow-up of approximately 20 years. Of these, 348 women with PCOS based on the Rotterdam criteria and 1,348 non-PCOS controls were followed to assess the age at which they reached menopause. An accelerated failure time survival regression model was used to identify the association between PCOS and the age at natural menopause (ANM), with and without adjustment for potential confounders. The unadjusted accelerated failure time survival model revealed a significant positive association between PCOS and ANM; PCOS women experienced time to menopause by a factor of 1.05 than non-PCOS controls (95% confidence interval, 1.02-1.06; P < 0.001). After adjusting for age at baseline, menarche age, history of hypertension, history of type 2 diabetes mellitus, parity, oral contraceptive use, body mass index, education level, physical activity, and smoking, the results remained significant (time ratio: 1.03; 95% confidence interval, 1.01-1.06; P = 0.002). This study indicates that ANM is significantly associated with PCOS in women. Our study findings may have implications for the fertility and reproductive health of women with PCOS. However, further large longitudinal studies on diverse populations accounting for other relevant confounders are still needed to provide data on the actual difference in age at menopause and to elucidate the underlying mechanisms of this association.

Abstract WP244: Myocardial Infarction Risk Following Stroke and Cervical Artery Dissection: Multi-State Inpatient Sample Analysis

Introduction: Cervical artery dissection (CAD) and acute ischemic stroke (AIS) are key health challenges. We utilized a multi-state database to examine myocardial infarction (MI) risk post stroke or CAD to estimate cardiovascular complication risk. Method: We analyzed State Inpatient Database from New York (2011-2017) and Florida (2011-2019). Adult patients with no head or neck trauma were separated into 4 groups based on diagnoses of AIS, CAD, concurrent AIS and CAD, or a reference group of transient ischemic attack, transient global amnesia, or migraine. Diagnosis identification was based on ICD-9 and ICD-10 CM codes. One-year MI risk was evaluated using Kaplan-Meier survival analysis, parametric accelerated failure time survival analysis, and additional Inverse Probability of Treatment Weighting (IPTW) analysis. Results: 827,761 patients were included (mean age 62.7 years, 61.58% female). Among all patients, 19,755 (2.39%) had MI within one-year: 1.4% of the reference group, 3.4% of the AIS group, 1.5% of the CAD group, and 1.8% of concurrent AIS and CAD group. Compared to the reference group, patients with AIS alone and patients with concurrent CAD and AIS had higher MI risk (hazard ratio [HR] 4.91, 95% CI 4.63-5.21, P &lt; 0.001; HR 1.67, 95% CI 1.02-2.73, P = 0.04, respectively), while patients with CAD alone had no elevated risk (HR 1.17, 95% CI 0.66-2.06, P = 0.60) (Figure). After IPTW adjustment for age, diabetes, heart failure, coronary artery disease and hyperlipidemia, patients with AIS alone still had the highest risk for MI (adjusted HR [aHR] 1.87, 95% CI 1.75-1.99, P &lt; 0.001), followed by patients with concurrent CAD and AIS (aHR 1.26, 95% CI 1.05-1.5, P = 0.012), while patients with CAD had nonsignificant risk (aHR 1.142, 95% CI 0.82-1.58, P = 0.42). Conclusion: This analysis reveals stroke patients are nearly two-fold more likely to have subsequent MI compared to those with CAD. These findings merit validation and may impact clinical evaluation and patient care.

Accelerated failure time survival model for prostate cancer

Background: Prostate cancer is the second most common cancer in men globally and ranks sixth among male cancer-related deaths. This study utilized survival analysis to assess post-diagnosis outcomes, aiming to enhance prostate cancer patient longevity. Insights were gained by determining median survival times, identifying prognostic factors, and applying an AFT model to prostate cancer data. Methods: The study collected data from 502 prostate cancer patients featured in a 1980 paper by D. P. Byar and S. B. Green, focusing on stage 3 or 4 cases. Over six years, they monitored these patients' survival under varying estrogen doses, analyzing 15 variables. Data import was done using excel, R and SPSS were used for the analysis. Utilizing the Kaplan-Meier method and AFT models, the study identified predictive factors and determined the most suitable model for prostate cancer analysis. Results: Among the 502 patients, 70.7% died, and 29.3% were right-censored, with a median survival of 35 months. Factors enhancing survival included a 1.0 mg estrogen dose, higher body weight, and elevated serum hemoglobin levels. In contrast, age, tumor size, cardiovascular disease history, and a composite index of stage and histology grade were associated with reduced survival odds in prostate cancer patients. Conclusions: Prostate cancer patients had a median survival of 35 months, with survival rates at 60 months (31.7%) and 75 months (23.2%). The 1.0 mg estrogen dose proved most effective among tested doses. The Weibull AFT model, with an AIC score of 3469.032, was the best-fit model.

Estimating quality adjusted life years in the absence of standard utility values – a dynamic joint modeling approach

Estimation of Quality Adjusted Life Years (QALYs) is pivotal toward cost-effectiveness analysis (CEA) of medical interventions. The popular multi-state decision analytic modeling approach to CEA uses standard utility values assigned to each disease state to estimate QALY. In this paper, we have formulated a new approach to estimate QALY by defining utility as a function of a longitudinal covariate significantly associated with disease progression. Association parameter between the longitudinal covariate and survival times has been estimated through joint modeling of the longitudinal and the Weibull accelerated failure time survival model. MCMC techniques have been used to predict expected survival times of each censored case using the fitted model. Time-dependent utility values, calculated using projected values of the longitudinal covariate, have been used to evaluate QALYs for each patient. Proposed methodology has been demonstrated on a retrospective survival data of HIV/AIDS patients. A simulation exercise has also been carried out to gauge the predictive capability of the joint model in projecting the values of the longitudinal covariate. Results show that the proposed dynamic approach to estimate QALY can be a promising alternative to the popular multi-state decision analytic modeling approach, especially when the standard utility values are not available.

Effect of Group Housing of Preweaned Dairy Calves: Health and Fecal Commensal Antimicrobial Resistance Outcomes.

The objectives of this study were to investigate the effects of group housing (three calves per group) on bovine respiratory disease (BRD), diarrhea and antimicrobial resistance (AMR) to fecal commensal Escherichia coli (EC) and enterococci/streptococci (ES). Our study comprised two arms, one experimental and one observational. In the experimental arm, preweaned calves on a California dairy were randomized to either individual (IND; n = 21) or group (GRP; n = 21) housing, using a modified California-style wooden hutch. The study period lasted from birth to 56 days of age, during which calves were health scored daily. Cumulative incidence and hazard ratios were estimated for disease. Antimicrobial resistance outcomes were assessed using a prospective cohort design; feces were collected from each calf three times per week and EC and ES were evaluated for AMR using the broth microdilution method against a panel of 19 antimicrobial drugs (AMD). Analysis of treatment records was used to select calves that had been exposed (EXP) to an AMD-treated calf. In GRP, exposure occurred when a calf was a hutchmate with an AMD-treated calf. In IND, exposure occurred when a calf was a neighbor with an AMD-treated calf (TRT). Age-matched unexposed calves (UNEXP) were then selected for comparison. Proportions of AMR in fecal commensals among EXP, UNEXP, and TRT calves were compared between GRP and IND. Accelerated failure time survival regression models were specified to compare differences in minimum inhibitory concentration (MIC) of fecal commensals between EXP and UNEXP calves within each of GRP and IND calves separately. Group calves had a BRD hazard 1.94 times greater that of IND calves (p = 0.03), using BRD treatment records as the outcome. For AMR in EC isolates, higher resistance to enrofloxacin was detected in enrofloxacin-EXP GRP isolates compared with enrofloxacin-EXP IND isolates, and UNEXP GRP calves had lower resistance to ceftiofur compared with enrofloxacin-EXP and enrofloxacin-TRT calves. A significant housing-by-time interaction was detected for EC ceftiofur MIC in EXP GRP calves at 4-14 days post exposure to enrofloxacin (MIC EXP-UNEXP: µg/mL (95% CI): 10.62 (1.17, 20.07)), compared with UNEXP calves. The findings of this study show an increase in BRD hazard for group-housed calves and an increase in ceftiofur resistance in group-housed calves exposed to an enrofloxacin-treated calf.

Bayesian parametric modeling of time to tuberculosis co-infection of HIV/AIDS patients at Jimma Medical Center, Ethiopia

Tuberculosis is the most common opportunistic infection among HIV/AIDS patients, including those following Antiretroviral Therapy treatment. The risk of tuberculosis infection is higher in people living with HIV/AIDS than in people who are free from HIV/AIDS. Many studies focused on prevalence and determinants of tuberculosis in HIV/AIDS patients without taking into account the censoring aspects of the time to event data. Therefore, this study was undertaken with aim to model time to tuberculosis co-infection of HIV/AIDS patients under follow-up at Jimma Medical Center, Ethiopia using Bayesian parametric survival models. A data of a retrospective cohort of 421 HIV/AIDS patients under follow-up from January 2016 to December 2020 until active tuberculosis was diagnosed or until the end of the study was collected from Jimma Medical Center, Ethiopia. The analysis of the data was performed using R-INLA software package. In order to identify the risk factors which have association with tuberculosis co-infection survival time, Bayesian parametric accelerated failure time survival models were fitted to the data using Integrated Nested Laplace Approximation methodology. About 26.37% of the study subjects had been co-infected with tuberculosis during the study period. Among the parametric accelerated failure time models, the Bayesian log-logistic accelerated failure time model was found to be the best fitting model for the data. Patients who lived in urban areas had shorter tuberculosis co-infection free survival time compared to those who lived in rural areas with an acceleration factor of 0.2842. Patients who smoke and drink alcohol had also shorter tuberculosis co-infection survival time than those who do not smoke and drink alcohol respectively. Patients with advanced WHO clinical stages(Stage III and IV), bedridden functional status, low body mass index and severe anemic status had shorter tuberculosis co-infection survival time. Place of residence, smoking, drinking alcohol, larger family size, advanced clinical stages(Stage III and Stage IV), bedridden functional status, CD4 count (le 200 cells/mm3 and 200–349 cells/mm3), low body mass index and low hemoglobin are the factors that lead to shorter tuberculosis co-infection survival time in HIV/AIDS patients. The findings of the study suggested us to forward the recommendations to modify patients’ life style, early screening and treatment of opportunistic diseases like anemia , as well as effective treatment and management of tuberculosis and HIV co-infection are important to prevent tuberculosis and HIV co-infection.

Modelling the Transition Process from Higher Education to Employment: The Case of Undergraduates from Debre Markos University

This study aimed to identify the factors that influence Debre Markos University bachelor degree graduates’ transition duration from higher education to employment. A retrospective study design and a total of 1448 undergraduates were considered in this study. Kaplan-Meier, Cox proportional hazard, and parametric accelerated failure time survival analysis were used to identify the factors that influence the duration of the job search of graduates. The best parametric model that could describe the duration of the job search to obtain the first job was chosen using the Akaike information criterion. As per the study, 50% of graduates were able to get their first employment 18 months after graduation. The Weibull parametric accelerated failure time model performed better in predicting the time it takes for graduates to find their first job. College, cumulative grade point average, father education, graduate region, public advertisement, starting own business, and doing an internship during the study were found to be statistically significant indicators of the transition duration of the graduates to obtain the first job. Debre Markos University 2019 graduates had a longer period of unemployment. The institution should have a fully functioning career service office that provides job search training and connects students with employers.

Interplay between polygenic propensity for ageing-related traits and the consumption of fruits and vegetables on future dementia diagnosis

BackgroundUnderstanding how polygenic scores for ageing-related traits interact with diet in determining a future dementia including Alzheimer’s diagnosis (AD) would increase our understanding of mechanisms underlying dementia onset.MethodsUsing 6784 population representative adults aged ≥50 years from the English Longitudinal Study of Ageing, we employed accelerated failure time survival model to investigate interactions between polygenic scores for AD (AD-PGS), schizophrenia (SZ-PGS) and general cognition (GC-PGS) and the baseline daily fruit and vegetable intake in association with dementia diagnosis during a 10-year follow-up. The baseline sample was obtained from waves 3–4 (2006–2009); follow-up data came from wave 5 (2010–2011) to wave 8 (2016–2017).ResultsConsuming < 5 portions of fruit and vegetables a day was associated with 33–37% greater risk for dementia in the following 10 years depending on an individual polygenic propensity. One standard deviation (1-SD) increase in AD-PGS was associated with 24% higher risk of dementia and 47% higher risk for AD diagnosis. 1-SD increase in SZ-PGS was associated with an increased risk of AD diagnosis by 66%(95%CI = 1.05–2.64) in participants who consumed < 5 portions of fruit or vegetables. There was a significant additive interaction between GC-PGS and < 5 portions of the baseline daily intake of fruit and vegetables in association with AD diagnosis during the 10-year follow-up (RERI = 0.70, 95%CI = 0.09–4.82; AP = 0.36, 95%CI = 0.17–0.66).ConclusionA diet rich in fruit and vegetables is an important factor influencing the subsequent risk of dementia in the 10 years follow-up, especially in the context of polygenetic predisposition to AD, schizophrenia, and general cognition.

Comparison of Cox proportional hazards model, Cox proportional hazards with time-varying coefficients model, and lognormal accelerated failure time model: Application in time to event analysis of melioidosis patients

Objective: To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time (AFT), Cox proportional hazards (PH), and Cox PH with time-varying coefficient (TVC) models.Methods: A retrospective study was conducted from 2014 to 2019 among 453 patients who were admitted to Hospital Sultanah Bahiyah, Kedah and Hospital Tuanku Fauziah, Perlis in Northern Malaysia due to confirmed-cultured melioidosis. The prognostic factors of mortality from melioidosis were obtained from AFT survival analysis, and Cox’s models and the findings were compared by using the goodness of fit methods. The analyses were done by using Stata SE version 14.0.Results: A total of 242 patients (53.4%) survived. In this study, the median survival time of melioidosis patients was 30.0 days (95% CI 0.0-60.9). Six significant prognostic factors were identified in the Cox PH model and Cox PH-TVC model. In AFT survival analysis, a total of seven significant prognostic factors were identified. The results were found to be only a slight difference between the identified prognostic factors among the models. AFT survival showed better results compared to Cox’s models, with the lowest Akaike information criteria and best fitted Cox-snell residuals.Conclusions: AFT survival analysis provides more reliable results and can be used as an alternative statistical analysis for determining the prognostic factors of mortality in melioidosis patients in certain situations.

Acute admission risk stratification of New Zealand primary care patients using demographic, multimorbidity, service usage and modifiable variables.

Introduction Risk stratification tools in primary care may help practices better identify high-risk patients and plan for their treatment. Patients of all ages can be at high risk of acute hospital admissions. Aim We aim to improve existing risk stratification tools by using larger datasets, and accounting for practice-level variations in hospitalisation rates and read-code quality. Methods This work has derived an acute admission risk stratification tool in the Wellington, Kāpiti Coast and Wairarapa regions of New Zealand. An open cohort, starting 1 March 2017 and finishing 1 November 2021, contains 319 943 patients. An accelerated failure time survival regression model is used to model acute admission risk. Candidate models are tested on holdout data using six different test metrics. Results Patient risk is most affected by demographic, and the frequency of recent healthcare system usage. Morbidity categories have less predictive capability, but may still be useful from a practical perspective. The preferred model has an area under the receiver operating characteristic curve (AUROC) of 0.75 for a 6-month forecast period. Discussion The model is straightforward to apply to other datasets. Although most of the highest-risk patients will be well-known to their primary care practices already, the model helps to identify the patients who are high risk but not regularly attendees of the practice, and may benefit from proactive care planning.

Keeping the driver in the loop through semi-automated or manual lane changes in conditionally automated driving

In the current study we investigated if drivers of conditionally automated vehicles can be kept in the loop through lane change maneuvers. More specifically, we examined whether involving drivers in lane-changes during a conditionally automated ride can influence critical take-over behavior and keep drivers’ gaze on the road. In a repeated measures driving simulator study (n = 85), drivers drove the same route three times, each trial containing four lane changes that were all either (1) automated, (2) semi-automated or (3) manual. Each ride ended with a critical take-over situation that could be solved by braking and/or steering. Critical take-over reactions were analyzed with a linear mixed model and parametric accelerated failure time survival analysis. As expected, semi-automated and manual lane changes throughout the ride led to 13.5% and 17.0% faster maximum deceleration compared to automated lane changes. Additionally, semi-automated and manual lane changes improved the quality of the take-over by significantly decreasing standard deviation of the steering wheel angle. Unexpectedly, drivers in the semi-automated condition were slowest to start the braking maneuver. This may have been caused by the drivers’ confusion as to how the semi-automated system would react. Additionally, the percentage gaze off-the-road was significantly decreased by the semi-automated (6.0%) and manual (6.6%) lane changes. Taken together, the results suggest that semi-automated and manual transitions may be an alarm-free instrument which developers could use to help maintain drivers’ perception–action loop and improve automated driving safety.

Length of Stay in ICU of Covid-19 Patients in England, March - May 2020

IntroductionLength of Stay (LoS) in Intensive Care Units (ICUs) is an important measure for planning beds capacity during the Covid-19 pandemic. However, as the pandemic progresses and we learn more about the disease, treatment and subsequent LoS in ICU may change.ObjectivesTo investigate the LoS in ICUs in England associated with Covid-19, correcting for censoring, and to evaluate the effect of known predictors of Covid-19 outcomes on ICU LoS.Data sourcesWe used retrospective data on Covid-19 patients, admitted to ICU between 6 March and 24 May, from the “Covid-19 Hospitalisation in England Surveillance System” (CHESS) database, collected daily from England’s National Health Service, and collated by Public Health England.MethodsWe used Accelerated Failure Time survival models with Weibull and log-normal distributional assumptions to investigate the effect of predictors, which are known to be associated with poor Covid-19 outcomes, on the LoS in ICU.ResultsPatients admitted before 25 March had significantly longer LoS in ICU (mean = 18.4 days, median = 12), controlling for age, sex, whether the patient received Extracorporeal Membrane Oxygenation, and a co-morbid risk factors score, compared with the period after 7 April (mean = 15.4, median = 10). The periods of admission reflected the changes in the ICU admission policy in England. Patients aged 50-65 had the longest LoS, while higher co-morbid risk factors score led to shorter LoS. Sex and ethnicity were not associated with ICU LoS.ConclusionsThe skew of the predicted LoS suggests that a mean LoS, as compared with median, might be better suited as a measure used to assess and plan ICU beds capacity. This is important for the ongoing second and any future waves of Covid-19 cases and potential pressure on the ICU resources. Also, changes in the ICU admission policy are likely to be confounded with improvements in clinical knowledge of Covid-19.

Prevalence of premature ovarian insufficiency and its determinants in Iranian populations: Tehran lipid and glucose study

BackgroundPremature ovarian insufficiency (POI) considered as a concerning health issue for women of reproductive age. In this study we aim to estimate the prevalence of POI and assessing the influential factors.MethodsData was obtained from Tehran lipid and glucose study (TLGS). All eligible post-menarcheal female participants of the TLGS, ages 20–65, were recruited (n = 6521). Participants were followed for the event of menopause, and age at menopause was recorded. Kaplan Meier analysis was applied to estimate mean and median for age at menopause. Weibull accelerated failure time survival regression model (AFT), was applied to assess influential determinants of POI. Conditional probability approach was used to provide estimation for prevalence of POI.ResultsIn this population-based study, the prevalence of POI (menopause age < 40 years) and early menopause (menopause age < 45 years) were estimated 3.5% and 24.6%, respectively. AFT model showed that in comparison to normal weight women, time to menopause was decreased by − 0.09 year (95% CI − 0.27, − 0.01, p = 0.023) and − 0.03 year (95% CI − 0.05, − 0.02, p = 0.000) in underweight and overweight women, respectively. Moreover, time to natural menopause was increased by 0.12 year (95% CI 0.07 to 0.17, p = 0.000) in women used oral contraceptives for > 6 months.ConclusionAbout one quartile of Iranian women experienced menopause at an age less than 45, especially the non-normal weight ones; this high prevalence is a critical public health concerns that needs to be addressed by health policy makers.

Accelerated Failure Time Survival Model to Analyze Morris Water Maze Latency Data

Traumatic brain injury (TBI) induces cognitive deficits clinically and in animal models. Learning and memory testing is critical when evaluating potential therapeutic strategies and treatments to manage the effects of TBI. We evaluated three data analysis methods for the Morris water maze (MWM), a learning and memory assessment widely used in the neurotrauma field, to determine which statistical tool is optimal for MWM data. Hidden platform spatial MWM data aggregated from three separate experiments from the same laboratory were analyzed using 1) a logistic regression model, 2) an analysis of variance (ANOVA) model, and 3) an accelerated failure time (AFT) time-to-event model. The logistic regression model showed no significant evidence of differences between treatments among any swims over all days of the study, p > 0.11. Although the ANOVA model found significant evidence of differences between sham and TBI groups on three out of four swims on the third day, results are potentially biased due to the failure of this model to account for censoring. The time-to-event AFT model showed significant differences between sham and TBI over all swims on the third day, p < 0.045, taking censoring into account. We suggest AFT models should be the preferred analytical methodology for latency to platform associated with MWM studies.

Extent of MGMT promoter methylation modifies the effect of temozolomide on overall survival in patients with glioblastoma: a regional cohort study.

Background MGMT methylation in glioblastoma predicts response to temozolomide but dichotomizing methylation status may mask the true prognostic value of quantitative MGMT methylation. This study evaluated whether extent of MGMT methylation interacts with the effect of temozolomide on overall survival.MethodsWe included consecutive glioblastoma patients aged ≥16 years diagnosed (April 2012–May 2020) at a neuro-oncology center. All patients had quantitative MGMT methylation measured using pyrosequencing. Those with MGMT methylated tumors were stratified into high and low methylation groups based on a cut-off using Youden index on 2-year survival. Our accelerated failure time survival models included extent of MGMT methylation, age, postoperative Karnofsky performance score, extent of resection, temozolomide regimen, and radiotherapy.ResultsThere were 414 patients. Optimal cut-off point using Youden index was 25.9% MGMT methylation. The number of patients in the unmethylated, low and high methylation groups was 223 (53.9%), 81 (19.6%), and 110 (26.6%), respectively. In the adjusted model, high (hazard ratio [HR] 0.60, 95% confidence intervals [CI] 0.46–0.79, P = 0.005) and low (HR 0.67, 95% CI 0.50–0.89, P < 0.001) methylation groups had better survival compared to unmethylated group. There was no evidence for interaction between MGMT methylation and completed temozolomide regimen (interaction term for low methylation P = 0.097; high methylation P = 0.071). This suggests no strong effect of MGMT status on survival in patients completing temozolomide regimen. In patients not completing the temozolomide regimen, higher MGMT methylation predicted better survival (interaction terms P < 0.001).ConclusionsQuantitative MGMT methylation may provide additional prognostic value. This is important when assessing clinical and research therapies.