Using multistate models with clinical trial data for a deeper understanding of complex disease processes.

A clinical trial represents a large commitment from all individuals involved and a huge financial obligation given its high cost; therefore, it is wise to make the most of all collected data by learning as much as possible. A multistate model is a generalized framework to describe longitudinal events; multistate hazards models can treat multiple intermediate/final clinical endpoints as outcomes and estimate the impact of covariates simultaneously. Proportional hazards models are fitted (one per transition), which can be used to calculate the absolute risks, that is, the probability of being in a state at a given time, the expected number of visits to a state, and the expected amount of time spent in a state. Three publicly available clinical trial datasets, colon, myeloid, and rhDNase, in the survival package in R were used to showcase the utility of multistate hazards models. In the colon dataset, a very well-known and well-used dataset, we found that the levamisole+fluorouracil treatment extended time in the recurrence-free state more than it extended overall survival, which resulted in less time in the recurrence state, an example of the classic "compression of morbidity." In the myeloid dataset, we found that complete response (CR) is durable, patients who received treatment B have longer sojourn time in CR than patients who received treatment A, while the mutation status does not impact the transition rate to CR but is highly influential on the sojourn time in CR. We also found that more patients in treatment A received transplants without CR, and more patients in treatment B received transplants after CR. In addition, the mutation status is highly influential on the CR to transplant transition rate. The observations that we made on these three datasets would not be possible without multistate models. We want to encourage readers to spend more time to look deeper into clinical trial data. It has a lot more to offer than a simple yes/no answer if only we, the statisticians, are willing to look for it.

Association of current substance use treatment with future reduced methamphetamine use in an observational cohort of men who have sex with men in Los Angeles.

INTRODUCTION: Methamphetamine use is highly prevalent among men who have sex with men (MSM), but knowledge of the long-term dynamics, and how they are affected by substance use treatment, is limited. This study aimed to describe trajectories of methamphetamine use among MSM, and to evaluate the impact of treatment for any kind of substance use on frequency of methamphetamine use. METHODS: This analysis used data from a cohort of MSM in Los Angeles, CA, who participated in semi-annual study visits from 2014 to 2022. The study characterized trajectories of methamphetamine use using a continuous time multistate Markov model with three states. States were defined using self-reported frequency of methamphetamine use in the past six months: frequent (daily), occasional (weekly or less), and never. The model estimated the association between receiving treatment for any kind of substance use and changes in state of frequency of methamphetamine use. RESULTS: This analysis included 2348 study visits among 285 individuals who were followed-up for an average of 4.4 years. Among participants who were in the frequent use state, 65 % (n = 26) of those who were receiving any kind of substance use treatment at a study visit had reduced their methamphetamine use at their next visit, compared to 33 % (n = 95) of those who were not receiving treatment. Controlling for age, race/ethnicity, and HIV-status, those who reported receiving current treatment for substance use were more likely to transition from occasional to no use (HR: 1.63, 95 % CI: 1.10-2.42) and frequent to occasional use (HR: 4.25, 95 % CI: 2.11-8.59) in comparison to those who did not report receiving current treatment for substance use. CONCLUSIONS: Findings from this dynamic modeling study provide a new method for assessing longitudinal methamphetamine use outcomes and add important evidence outside of clinical trials that substance use treatment may reduce methamphetamine use.

Comparison of nonparametric estimators of the expected number of recurrent events.

We compare the performance of nonparametric estimators for the mean number of recurrent events and provide a systematic overview for different recurrent event settings. The mean number of recurrent events is an easily interpreted marginal feature often used for treatment comparisons in clinical trials. Incomplete observations, dependencies between successive events, terminating events acting as competing risk, or gaps between at risk periods complicate the estimation. We use survival multistate models to represent different complex recurrent event situations, profiting from recent advances in nonparametric estimation for non-Markov multistate models, and explain several estimators by using multistate intensity processes, including the common Nelson-Aalen-type estimators with and without competing mortality. In addition to building on estimation of state occupation probabilities in non-Markov models, we consider a simple extension of the Nelson-Aalen estimator by allowing for dependence on the number of prior recurrent events. We pay particular attention to the assumptions required for the censoring mechanism, one issue being that some settings require the censoring process to be entirely unrelated while others allow for state-dependent or event-driven censoring. We conducted extensive simulation studies to compare the estimators in various complex situations with recurrent events. Our practical example deals with recurrent chronic obstructive pulmonary disease exacerbations in a clinical study, which will also be used to illustrate two-sample-inference using resampling.

Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis.

BACKGROUND: Dementia is defined as a cognitive decline that affects functional status. Longitudinal ageing surveys often lack a clinical diagnosis of dementia though measure cognition and daily function over time. We used unsupervised machine learning and longitudinal data to identify transition to probable dementia. METHODS: Multiple Factor Analysis was applied to longitudinal function and cognitive data of 15,278 baseline participants (aged 50 years and more) from the Survey of Health, Ageing, and Retirement in Europe (SHARE) (waves 1, 2 and 4-7, between 2004 and 2017). Hierarchical Clustering on Principal Components discriminated three clusters at each wave. We estimated probable or "Likely Dementia" prevalence by sex and age, and assessed whether dementia risk factors increased the risk of being assigned probable dementia status using multistate models. Next, we compared the "Likely Dementia" cluster with self-reported dementia status and replicated our findings in the English Longitudinal Study of Ageing (ELSA) cohort (waves 1-9, between 2002 and 2019, 7840 participants at baseline). RESULTS: Our algorithm identified a higher number of probable dementia cases compared with self-reported cases and showed good discriminative power across all waves (AUC ranged from 0.754 [0.722-0.787] to 0.830 [0.800-0.861]). "Likely Dementia" status was more prevalent in older people, displayed a 2:1 female/male ratio, and was associated with nine factors that increased risk of transition to dementia: low education, hearing loss, hypertension, drinking, smoking, depression, social isolation, physical inactivity, diabetes, and obesity. Results were replicated in ELSA cohort with good accuracy. CONCLUSIONS: Machine learning clustering can be used to study dementia determinants and outcomes in longitudinal population ageing surveys in which dementia clinical diagnosis is lacking.

Transitions in health care settings for frequent and infrequent users of emergency departments: a population-based retrospective cohort study.

BACKGROUND: Efforts to reduce emergency department (ED) volumes often target frequent users. We examined transitions in care across ED, hospital, and community settings, and in-hospital death, for high system users (HSUs) compared to controls. METHODS: Population-based databases provided ED visits and hospitalizations in Alberta and Ontario, Canada. The retrospective cohort included the top 10% of all the ED users during 2015/2016 (termed HSUs) and a random sample of controls (4 per each HSU) from the bottom 90% per province. Rates of transitions among ED, hospitalization, community settings, and in-hospital mortality were adjusted for sociodemographic and ED variables in a multistate statistical model. RESULTS: There were 2,684,924 patients and 579,230 (21.6%) were HSUs. Patient characteristics associated with shorter community to ED transition times for HSUs included Alberta residence (ratio of hazard ratio [RHR] = 1.11, 95% confidence interval [CI] 1.11,1.12), living in areas in the lower income quintile (RHR = 1.06, 95%CI 1.06,1.06), and Ontario residents without a primary health care provider (RHR = 1.13, 95%CI 1.13,1.14). Once at the ED, characteristics associated with shorter ED to hospital transition times for HSUs included higher acuity (e.g., RHR = 1.70, 95% CI 1.61, 1.81 for emergent), and for many diagnoses including chest pain (RHR = 1.71, 95%CI 1.65,1.76) and gastrointestinal (RHR = 1.66, 95%CI 1.62,1.71). Once admitted to hospital, HSUs did not necessarily have longer stays except for conditions such as chest pain (RHR = 0.90, 95% CI 0.86, 0.95). HSUs had shorter times to death in the ED if they presented for cancer (RHR = 2.51), congestive heart failure (RHR = 1.93), myocardial infarction (RHR = 1.53), and stroke (RHR = 1.84), and shorter times to death in-hospital if they presented with cancer (RHR = 1.29). CONCLUSIONS: Differences between HSUs and controls in predictors of transitions among care settings were identified. Co-morbidities and limitations in access to primary care are associated with more rapid transitions from community to ED and hospital among HSUs. Interventions targeting these challenges may better serve patients across health systems.. TRIAL REGISTRATION: Not applicable.

Evaluation Factors Affecting on Recurrence, Metastasis, and Survival of Breast Cancer in Iranian Women by Multi-State Model Approach.

Background: We used the multistate model to investigate how prognostic factors of breast cancer are seen to affect the disease process. Methods: This cohort study was conducted at Motamed Cancer Institute of Tehran, Iran on 2363 breast cancer patients admitted from 1978 to 2017, and they were followed up until 2018. We applied the multistate models, including four states: diagnosis, recurrence, metastasis, and final absorbing mortality state. Results: Age over 50 years, positive lymph nodes and tumor size intensified the hazard of transition from diagnosis to metastasis (P=0.002, P<0.001 and P=0.001 respectively) and they also intensified the hazard of transition from diagnosis to mortality (P=0.010, P<0.001 and P<0.001 respectively). At the same time, the educational level decreased the hazard of mentioned transitions (P<0.001). Positive estrogen receptors reduced the hazard of transition from diagnosis to metastasis (P=0.007) and positive lymph nodes also intensified the hazard of transition from metastasis to mortality (P=0.040). Tumor size had an increasing role in the transitions from diagnosis to recurrence, recurrence to metastasis, and metastasis to mortality (P=0.014, P=0.018 and P=0.002 respectively). Conclusion: Multistate model presented the detailed effects of prognostic factors on progression of breast cancer. Implementing early diagnosis strategies and providing informational programs, especially in younger ages and lower educational level patients may be helpful in reducing the hazard of transition to higher states of breast cancer and increasing the survival of Iranian women with breast cancer by controlling tumor size growth, lymph nodes involvements and estrogen receptor status.

Racial, Ethnic, Nativity, and Educational Disparities in Cognitive Impairment and Activity Limitations in the United States, 1998-2016.

Despite extensive research on cognitive impairment and limitations in basic activities of daily living, no study has investigated the burden of their co-occurrence (co-impairment). Using the Health and Retirement Study data and incidence-based multistate models, we study the population burden of co-impairment using three key indicators: mean age at onset, lifetime risk, and health expectancy. We examine patterns by gender, race, ethnicity, nativity, education, and their interactions for U.S. residents aged 50-100. Furthermore, we analyze what fractions of racial, ethnic, and nativity disparities in co-impairment are attributable to inequalities in educational attainment. Results reveal that an estimated 56% of women and 41% of men aged 50 will experience co-impairment in their remaining life expectancy. Men experience an earlier onset of co-impairment than women (74 vs. 77 years), and women live longer in co-impairment than men (3.4 vs. 1.9 years). Individuals who are Black, Latinx, and lower educated, especially those experiencing intersecting disadvantages, have substantially higher lifetime risk of co-impairment, earlier co-impairment onset, and longer life in co-impairment than their counterparts. Up to 75% of racial, ethnic, and nativity disparity is attributable to inequality in educational attainment. This study provides novel insights into the burden of co-impairment and offers evidence of dramatic disparities in the older U.S. population.

Pseudo-value regression of clustered multistate current status data with informative cluster sizes.

Multistate current status data presents a more severe form of censoring due to the single observation of study participants transitioning through a sequence of well-defined disease states at random inspection times. Moreover, these data may be clustered within specified groups, and informativeness of the cluster sizes may arise due to the existing latent relationship between the transition outcomes and the cluster sizes. Failure to adjust for this informativeness may lead to a biased inference. Motivated by a clinical study of periodontal disease, we propose an extension of the pseudo-value approach to estimate covariate effects on the state occupation probabilities for these clustered multistate current status data with informative cluster or intra-cluster group sizes. In our approach, the proposed pseudo-value technique initially computes marginal estimators of the state occupation probabilities utilizing nonparametric regression. Next, the estimating equations based on the corresponding pseudo-values are reweighted by functions of the cluster sizes to adjust for informativeness. We perform a variety of simulation studies to study the properties of our pseudo-value regression based on the nonparametric marginal estimators under different scenarios of informativeness. For illustration, the method is applied to the motivating periodontal disease dataset, which encapsulates the complex data-generation mechanism.

MSMpred: interactive modelling and prediction of individual evolution via multistate models.

BACKGROUND: Modelling the course of a disease regarding severe events and identifying prognostic factors is of great clinical relevance. Multistate models (MSM) can be used to describe diseases or processes that change over time using different states and the transitions between them. Specifically, they are useful to analyse a disease with an increasing degree of severity, that may precede death. The complexity of these models changes depending on the number of states and transitions taken into account. Due to that, a web tool has been developed making easier to work with those models. RESULTS: MSMpred is a web tool created with the shiny R package that has two main features: 1) to allow to fit a MSM from specific data; 2) to predict the clinical evolution for a given subject. To fit the model, the data to be analysed must be upload in a prespecified format. Then, the user has to define the states and transitions as well as the covariates (e.g., age or gender) involved in each transition. From this information, the app returns histograms or barplots, as appropriate, to represent the distributions of the selected covariates and boxplots to show the patient' length of stay (for uncensored data) in each state. To make predictions, the values of selected covariates from a new subject at baseline has to be provided. From these inputs, the app provides some indicators of the subject's evolution such as the probability of 30-day death or the most likely state at a fixed time. Furthermore, visual representations (e.g., the stacked transition probabilities plot) are given to make predictions more understandable. CONCLUSIONS: MSMpred is an intuitive and visual app that eases the work of biostatisticians and facilitates to the medical personnel the interpretation of MSMs.

Flexible transition timing in discrete-time multistate life tables using Markov chains with rewards.

Discrete-time multistate life tables are attractive because they are easier to understand and apply in comparison with their continuous-time counterparts. While such models are based on a discrete time grid, it is often useful to calculate derived magnitudes (e.g. state occupation times), under assumptions which posit that transitions take place at other times, such as mid-period. Unfortunately, currently available models allow very few choices about transition timing. We propose the use of Markov chains with rewards as a general way of incorporating information on the timing of transitions into the model. We illustrate the usefulness of rewards-based multistate life tables by estimating working life expectancies using different retirement transition timings. We also demonstrate that for the single-state case, the rewards approach matches traditional life-table methods exactly. Finally, we provide code to replicate all results from the paper plus R and Stata packages for general use of the method proposed.

Adjusting for informative cluster size in pseudo-value-based regression approaches with clustered time to event data.

Informative cluster size (ICS) arises in situations with clustered data where a latent relationship exists between the number of participants in a cluster and the outcome measures. Although this phenomenon has been sporadically reported in the statistical literature for nearly two decades now, further exploration is needed in certain statistical methodologies to avoid potentially misleading inferences. For inference about population quantities without covariates, inverse cluster size reweightings are often employed to adjust for ICS. Further, to study the effect of covariates on disease progression described by a multistate model, the pseudo-value regression technique has gained popularity in time-to-event data analysis. We seek to answer the question: "How to apply pseudo-value regression to clustered time-to-event data when cluster size is informative?" ICS adjustment by the reweighting method can be performed in two steps; estimation of marginal functions of the multistate model and fitting the estimating equations based on pseudo-value responses, leading to four possible strategies. We present theoretical arguments and thorough simulation experiments to ascertain the correct strategy for adjusting for ICS. A further extension of our methodology is implemented to include informativeness induced by the intracluster group size. We demonstrate the methods in two real-world applications: (i) to determine predictors of tooth survival in a periodontal study and (ii) to identify indicators of ambulatory recovery in spinal cord injury patients who participated in locomotor-training rehabilitation.

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19.

Objective: This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). Material and methods: The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. Results: The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. Discussion: In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. Conclusions: Our easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.

On restricted mean time in favor of treatment.

The restricted mean time in favor (RMT-IF) of treatment is a nonparametric effect size for complex life history data. It is defined as the net average time the treated spend in a more favorable state than the untreated over a prespecified time window. It generalizes the familiar restricted mean survival time (RMST) from the two-state life-death model to account for intermediate stages in disease progression. The overall estimand can be additively decomposed into stage-wise effects, with the standard RMST as a component. Alternate expressions of the overall and stage-wise estimands as integrals of the marginal survival functions for a sequence of landmark transitioning events allow them to be easily estimated by plug-in Kaplan-Meier estimators. The dynamic profile of the estimated treatment effects as a function of follow-up time can be visualized using a multilayer, cone-shaped "bouquet plot." Simulation studies under realistic settings show that the RMT-IF meaningfully and accurately quantifies the treatment effect and outperforms traditional tests on time to the first event in statistical efficiency thanks to its fuller utilization of patient data. The new methods are illustrated on a colon cancer trial with relapse and death as outcomes and a cardiovascular trial with recurrent hospitalizations and death as outcomes. The R-package rmt implements the proposed methodology and is publicly available from the Comprehensive R Archive Network (CRAN).

Long-term Outcome Reporting in Older Kidney Transplant Recipients and the Limitations of Conventional Survival Metrics.

Introduction: The kidney transplant recipient population in the United States is aging rapidly, which may exacerbate some of the limitations of conventional outcome metrics. Methods: Using data from the Scientific Registry of Transplant Recipients (SRTR), age-stratified unadjusted Kaplan-Meier and competing risk survival analyses were performed on a cohort of 238,123 adult recipients of a first-time single kidney transplant between 2000 and 2017. These were compared with a multistate model incorporating 5 post-transplant states (alive with functioning graft, death with functioning graft, graft failed (alive), retransplanted, and death after graft failure). Results: Kaplan-Meier resulted in an age-dependent overestimation of the risks of graft failure and death with functioning graft, compared with competing risk or multistate models. In elderly (≥75 years old) recipients, the absolute overestimation of the risk of death with functioning graft was 4-fold higher than in those younger than 55 years. The multistate model demonstrated that for patients transplanted at age 55 years and older, the probability of being back on dialysis was never more than 4% at any point post-transplant. The underlying reasons were low graft failure rates and high mortality after resuming dialysis as follows: 2-year mortality after graft failure was 38%, 54%, and 67% in recipients aged from 55 to 64 years, from 65 to 74 years, and those aged 75 years and older, versus 20% in those younger than 55 years. Conclusion: Multistate models provide an accurate and comprehensive assessment of the life course of kidney transplant recipients. This may be particularly relevant in older recipients, who are more prone to event rate overestimation and for whom outcomes after graft failure are substantially worse than for younger recipients.

The Utility of Multistate Models: A Flexible Framework for Time-to-Event Data.

Purpose of Review: Survival analyses are common and essential in medical research. Most readers are familiar with Kaplan-Meier curves and Cox models; however, very few are familiar with multistate models. Although multistate models were introduced in 1965, they only recently receive more attention in the medical research community. The current review introduces common terminologies and quantities that can be estimated from multistate models. Examples from published literature are used to illustrate the utility of multistate models. Recent Findings: A figure of states and transitions is a useful depiction of a multistate model. Clinically meaningful quantities that can be estimated from a multistate model include the probability in a state at a given time, the average time in a state, and the expected number of visits to a state; all of which describe the absolute risks of an event. Relative risk can also be estimated using multistate hazard models. Summary: Multistate models provide a more general and flexible framework that extends beyond the Kaplan-Meier estimator and Cox models. Multistate models allow simultaneous analyses of multiple disease pathways to provide insights into the natural history of complex diseases. We strongly encourage the use of multistate models when analyzing time-to-event data. Supplementary Information: The online version contains supplementary material available at 10.1007/s40471-022-00291-y.

Unraveling the Treatment Effect of Baricitinib on Clinical Progression and Resource Utilization in Hospitalized COVID-19 Patients: Secondary Analysis of the Adaptive COVID-19 Treatment Randomized Trial-2.

Background: The Adaptive COVID Treatment Trial-2 (ACTT-2) found that baricitinib in combination with remdesivir therapy (BCT) sped recovery in hospitalized coronavirus disease 2019 (COVID-19) patients vs remdesivir monotherapy (RMT). We examined how BCT affected progression throughout hospitalization and utilization of intensive respiratory therapies. Methods: We characterized the clinical trajectories of 891 ACTT-2 participants requiring supplemental oxygen or higher levels of respiratory support at enrollment. We estimated the effect of BCT on cumulative incidence of clinical improvement and deterioration using competing risks models. We developed multistate models to estimate the effect of BCT on clinical improvement and deterioration and on utilization of respiratory therapies. Results: BCT resulted in more linear improvement and lower incidence of clinical deterioration compared with RMT (hazard ratio [HR], 0.74; 95% CI, 0.58 to 0.95). The benefit was pronounced among participants enrolled on high-flow oxygen or noninvasive positive-pressure ventilation. In this group, BCT sped clinical improvement (HR, 1.21; 95% CI, 0.99 to 1.51) while slowing clinical deterioration (HR, 0.71; 95% CI, 0.48 to 1.02), which reduced the expected days in ordinal score (OS) 6 per 100 patients by 74 days (95% CI, -8 to 154 days) and the expected days in OS 7 per 100 patients by 161 days (95% CI, 46 to 291 days) compared with RMT. BCT did not benefit participants who were mechanically ventilated at enrollment. Conclusions: Compared with RMT, BCT reduces the clinical burden and utilization of intensive respiratory therapies for patients requiring low-flow oxygen or noninvasive positive-pressure ventilation compared with RMT and may thereby improve care for this patient population.

Transitions Between Preexposure Prophylaxis Eligibility States and HIV Infection in the Lisbon Cohort of HIV-Negative Men Who Have Sex With Men: A Multistate Model Analysis.

We aimed to describe transitions between preexposure prophylaxis (PrEP) eligibility and human immunodeficiency virus (HIV) infection among HIV-negative men who have sex with men (MSM). We used data from 1,885 MSM, who had not used PrEP, enrolled in the Lisbon Cohort of MSM, with at least 2 consecutive measurements of PrEP eligibility from 2014-2020. A time-homogeneous Markov multistate model was applied to describe the transitions between states of PrEP eligibility-eligible and ineligible-and from these to HIV infection (HIV). The intensities of the transitions were closer for ineligible-to-eligible and eligible-to-ineligible transitions (intensity ratio, 1.107, 95% confidence interval (CI): 1.080, 1.176), while the intensity of the eligible-to-HIV transition was higher than that for ineligible-to-HIV transition (intensity ratio, 9.558, 95% CI: 0.738, 65.048). The probabilities of transitions increased with time; for 90 days, the probabilities were similar for the ineligible-to-eligible and eligible-to-ineligible transitions (0.285 (95% CI: 0.252, 0.319) vs. 0.258 (95% CI: 0.228, 0.287)), while the eligible-to-HIV transition was more likely than ineligible-to-HIV (0.004 (95% CI: 0.003, 0.007) vs. 0.001 (95% CI: 0.001, 0.008)) but tended to become closer with time. Being classified as ineligible was a short-term indicator of a lower probability of acquiring HIV. Once an individual moved to eligible, he was at a higher risk of seroconversion, demanding a timely delivery ofPrEP.

Deconstructing the Treatment Effect of Remdesivir in the Adaptive Coronavirus Disease 2019 (COVID-19) Treatment Trial-1: Implications for Critical Care Resource Utilization.

BACKGROUND: The Adaptive Coronavirus Disease 2019 (COVID-19) Treatment Trial-1 (ACTT-1) found that remdesivir therapy hastened recovery in patients hospitalized with COVID-19, but the pathway for this improvement was not explored. We investigated how the dynamics of clinical progression changed along 4 pathways: recovery, improvement in respiratory therapy requirement, deterioration in respiratory therapy requirement, and death. METHODS: We analyzed trajectories of daily ordinal severity scores reflecting oxygen requirements of 1051 patients hospitalized with COVID-19 who participated in ACTT-1. We developed competing risks models that estimate the effect of remdesivir therapy on cumulative incidence of clinical improvement and deterioration, and multistate models that utilize the entirety of each patient's clinical course to characterize the effect of remdesivir on progression along the 4 pathways above. RESULTS: Based on a competing risks analysis, remdesivir reduced clinical deterioration (hazard ratio [HR], 0.73; 95% confidence interval [CI]: .59-.91) and increased clinical improvement (HR, 1.22; 95% CI: 1.08, 1.39) relative to baseline. Our multistate models indicate that remdesivir inhibits worsening to ordinal scores of greater clinical severity among patients on room air or low-flow oxygen (HR, 0.74; 95% CI: .57-.94) and among patients receiving mechanical ventilation or high-flow oxygen/noninvasive positive-pressure ventilation (HR, 0.73; 95% CI: .53-1.00) at baseline. We also find that remdesivir reduces expected intensive care respiratory therapy utilization among patients not mechanically ventilated at baseline. CONCLUSIONS: Remdesivir speeds time to recovery by preventing worsening to clinical states that would extend the course of hospitalization and increase intensive respiratory support, thereby reducing the overall demand for hospital care.

Evaluating the transitions in care for children presenting with acute asthma to emergency departments: a retrospective cohort study.

BACKGROUND: Acute asthma is a common presentation to emergency departments (EDs) worldwide and, due to overcrowding, delays in treatment often occur. This study deconstructs the total ED length of stay into stages and estimates covariate effects on transition times for children presenting with asthma. METHODS: We extracted ED presentations in 2019 made by children in Alberta, Canada for acute asthma. We used multivariable Cox regressions in a multistate model to model transition times among the stages of start, physician initial assessment (PIA), disposition decision, and ED departure. RESULTS: Data from 6598 patients on 8270 ED presentations were extracted. The individual PIA time was longer (i.e., HR < 1) when time to the crowding metric (hourly PIA) was above 1 h (HR = 0.32; 95% CI:0.30,0.34), for tertiary (HR = 0.65; 95% CI:0.61,0.70) and urban EDs (HR = 0.77; 95% CI:0.70,0.84), for younger patients (HR = 0.99 per year; 95% CI:0.99,1.00), and for patients triaged less urgent/non-urgent (HR = 0.89; 95% CI:0.84,0.95). It was shorter for patients arriving by ambulance (HR = 1.22; 95% CI:1.04,1.42). Times from PIA to disposition decision were longer for tertiary (HR = 0.47; 95% CI:0.44,0.51) and urban (HR = 0.69; 95% CI:0.63,0.75) EDs, for patients triaged as resuscitation/emergent (HR = 0.51; 95% CI:0.48,0.54), and for patients arriving by ambulance (HR = 0.78; 95% CI:0.70,0.87). Times from disposition decision to ED departure were longer for patients who were admitted (HR = 0.16; 95% CI:0.13,0.20) or transferred (HR = 0.42; 95% CI:0.35,0.50), and for tertiary EDs (HR = 0.93; 95% CI:0.92,0.94). CONCLUSIONS: All transition times were impacted by ED presentation characteristics. The sole key patient characteristic was age and it only impacted time to PIA. ED crowding demonstrated strong effects of time to PIA but not for the transition times involving disposition decision and ED departure stages.

Markov modelling of viral load adjusting for CD4 orthogonal variable and multivariate conditional autoregressive mapping of the HIV immunological outcomes among ART patients in Zimbabwe.

BACKGROUND: This study aimed to jointly model HIV disease progression patterns based on viral load (VL) among adult ART patients adjusting for the time-varying "incremental transients states" variable, and the CD4 cell counts orthogonal variable in a single 5-stage time-homogenous multistate Markov model. We further jointly mapped the relative risks of HIV disease progression outcomes (detectable VL (VL ≥ 50copies/uL) and immune deterioration (CD4 < 350cells/uL) at the last observed visit) conditional not to have died or become loss to follow-up (LTFU). METHODS: Secondary data analysis of individual-level patients on ART was performed. Adjusted transition intensities, hazard ratios (HR) and regression coefficients were estimated from the joint multistate model of VL and CD4 cell counts. The mortality and LTFU transition rates defined the extent of patients' retention in care. Joint mapping of HIV disease progression outcomes after ART initiation was done using the Bayesian intrinsic Multivariate Conditional Autoregressive prior model. RESULTS: The viral rebound from the undetectable state was 1.78times more likely compared to viral suppression among patients with VL ranging from 50-1000copies/uL. Patients with CD4 cell counts lower than expected had a higher risk of viral increase above 1000copies/uL and death if their VL was above 1000copies/uL (state 2 to 3 (λ23): HR = 1.83 and (λ34): HR = 1.42 respectively). Regarding the time-varying effects of CD4 cell counts on the VL transition rates, as the VL increased, (λ12 and λ23) the transition rates increased with a decrease in the CD4 cell counts over time. Regardless of the individual's VL, the transition rates to become LTFU decreased with a decrease in CD4 cell counts. We observed a strong shared geographical pattern of 66% spatial correlation between the relative risks of detectable VL and immune deterioration after ART initiation, mainly in Matabeleland North. CONCLUSION: With high rates of viral rebound, interventions which encourage ART adherence and continual educational support on the barriers to ART uptake are crucial to achieve and sustain viral suppression to undetectable levels. Area-specific interventions which focus on early ART screening through self-testing, behavioural change campaigns and social support strategies should be strengthened in heavily burdened regions to sustain the undetectable VL. Sustaining undetectable VL lowers HIV transmission in the general population and this is a step towards achieving zero HIV incidences by 2030.