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Trying to better define Bipolar Disorder (BD) progression, different staging models have been conceptualized, each one emphasizing different aspects of illness. In a previous article we retrospectively applied the main staging models to a sample of 100 bipolar patients at four time points over a ten-year observation. In the present study, focusing on Kupka & Hillegers's model, we aimed to assess the transition of the same sample through the different stages of illness and to explore the potential role of clinical variables on the risk of progression. Multistate Model using the mstate package in R and Markov model with stratified hazards were used for statistical analysis. A high hazard of transition from stage 2 to 3 emerged, with a probability of staying in stage 2 decreasing to 14 % after 3 years. BD II was significantly associated with transition from stage 1 to 2, whereas the number of lifetime episodes >3 and the elevated predominant polarity with transition from stage 3 to 4. Our results corroborated the evidence on BD progression and contributed to outline its trajectory over time. Further effort may help to define a standardized staging approach towards ever increasing tailored interventions. • We retrospectively applied Kupka & Hillegers's model to a sample of 100 bipolar patients at 4 time-points over 10 years • We assessed the transition across stages and the role of clinical variables on the risk of progression • A high hazard of transition 2→3 emerged, with a probability of staying in stage 2 decreasing to 14% after 3 years • BD II was associated with transition 1→2; > 3 lifetime episodes and elevated predominant polarity with transition 3→4 [ABSTRACT FROM AUTHOR]

Preeclampsia is a pregnancy‐associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time‐to‐event setting of "semi‐competing risks" in which a non‐terminal event of interest is subject to the occurrence of a terminal event of interest. The semi‐competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non‐terminal event, which we call "clinically immediate" and "non‐immediate" terminal events. Though such phenomena arise in many clinical contexts, to‐date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty‐based illness‐death model with a binary submodel to distinguish risk of immediate terminal event following the non‐terminal event. The model admits direct dependence of the terminal event on the non‐terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions. [ABSTRACT FROM AUTHOR]

Purpose: To examine the effects of fresh fruit, dried fruit, raw vegetables, and cooked vegetables on type 2 diabetes (T2D) progression trajectory. Methods: We included 429,886 participants in the UK Biobank who were free of diabetes and diabetes complications at baseline. Food groups were determined using a validated food frequency questionnaire. Outcomes were T2D incidence, complications, and mortality. Multi-state model was used to analyze the effects of food groups on T2D progression. Results: During a follow-up of 12.6 years, 10,333 incident T2D cases were identified, of whom, 3961 (38.3%) developed T2D complications and 1169 (29.5%) died. We found that impacts of four food groups on T2D progression varied depending on disease stage. For example, compared to participants who ate less than one piece of dried fruit per day, the hazard ratios and 95% confidence intervals for those who ate ≥ 2 pieces of dried fruit per day were 0.82 (0.77, 0.87), 0.88 (0.85, 0.92), and 0.86 (0.78, 0.95) for transitions from diabetes-free state to incident T2D, from diabetes-free state to total death, and from incident T2D to T2D complications, respectively. Higher intake of fresh fruit was significantly associated with lower risk of disease progression from diabetes-free state to all-cause death. Higher intake of raw and cooked vegetables was significantly associated with lower risks of disease progression from diabetes-free state to incident T2D and to total death. Conclusions: These findings indicate that higher intake of fresh fruit, dried fruit, raw vegetables, and cooked vegetables could be beneficial for primary and secondary prevention of T2D. [ABSTRACT FROM AUTHOR]

Abstract Background To investigate the association between cigarette smoking, smoking cessation and the trajectory of cardiometabolic multimorbidity (CMM), and further to examine the association of age at smoking initiation and smoking cessation with CMM. Methods This study included 298,984 UK Biobank participants without cardiometabolic diseases (CMDs) (including type 2 diabetes, coronary heart diseases, stroke, and hypertension) at baseline. Smoking status was categorized into former, current, and never smokers, with age at smoking initiation and smoking cessation as a proxy for current and former smokers. The multi-state model was performed to evaluate the association between cigarette smoking, smoking cessation and CMM. Results During a median follow-up of 13.2 years, 59,193 participants developed first cardiometabolic disease (FCMD), 14,090 further developed CMM, and 16,487 died. Compared to former smokers, current smokers had higher risk at all transitions, with hazard ratio (95% confidence interval) = 1.59 (1.55 ∼ 1.63) vs. 1.18 (1.16 ∼ 1.21) (P = 1.48 × 10− 118) from health to FCMD, 1.40 (1.33 ∼ 1.47) vs. 1.09 (1.05 ∼ 1.14) (P = 1.50 × 10− 18) from FCMD to CMM, and 2.87 (2.72 ∼ 3.03) vs. 1.38 (1.32 ∼ 1.45) (P

Background: To investigate the association between cigarette smoking, smoking cessation and the trajectory of cardiometabolic multimorbidity (CMM), and further to examine the association of age at smoking initiation and smoking cessation with CMM. Methods: This study included 298,984 UK Biobank participants without cardiometabolic diseases (CMDs) (including type 2 diabetes, coronary heart diseases, stroke, and hypertension) at baseline. Smoking status was categorized into former, current, and never smokers, with age at smoking initiation and smoking cessation as a proxy for current and former smokers. The multi-state model was performed to evaluate the association between cigarette smoking, smoking cessation and CMM. Results: During a median follow-up of 13.2 years, 59,193 participants developed first cardiometabolic disease (FCMD), 14,090 further developed CMM, and 16,487 died. Compared to former smokers, current smokers had higher risk at all transitions, with hazard ratio (95% confidence interval) = 1.59 (1.55 ∼ 1.63) vs. 1.18 (1.16 ∼ 1.21) (P = 1.48 × 10− 118) from health to FCMD, 1.40 (1.33 ∼ 1.47) vs. 1.09 (1.05 ∼ 1.14) (P = 1.50 × 10− 18) from FCMD to CMM, and 2.87 (2.72 ∼ 3.03) vs. 1.38 (1.32 ∼ 1.45) (P < 0.001) from health, 2.16 (1.98 ∼ 2.35) vs. 1.25 (1.16 ∼ 1.34) (P = 1.18 × 10− 46) from FCMD, 2.02 (1.79 ∼ 2.28) vs. 1.22 (1.09 ∼ 1.35) (P = 3.93 × 10− 17) from CMM to death; whereas quitting smoking reduced the risk attributed to cigarette smoking by approximately 76.5% across all transitions. Reduced risks of smoking cessation were also identified when age at quitting smoking was used as a proxy for former smokers. Conclusions: Cigarette smoking was associated with a higher risk of CMM across all transitions; however, smoking cessation, especially before the age of 35, was associated with a significant decrease in CMM risk attributed to cigarette smoking. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the association of body mass index (BMI) with risk of first cardiometabolic disease (FCMD), cardiometabolic multimorbidity (CMM) and death. Methods: 87,512 participants free of CMD were included from the Kailuan cohort, which was established during 2006–2007 and followed up until 2020. BMI was classified as underweight (< 18.5 kg/m2), healthy weight (18.5–23.9 kg/m2), overweight (24.0–27.9 kg/m2), mildly obese (28.0–31.9 kg/m2), and severely obese (≥ 32.0 kg/m2). FCMD was defined as the first onset of diabetes, heart disease, or stroke, and CMM as the coexistence of at least two CMD. The hazard ratio (HR) and 95% confidence interval (95%CI) were estimated with multi-state models. Results: 20,577 participants developed FCMD, 2232 developed CMM afterwards, and 10,191 died. Individuals with higher BMI was more likely to develop FCMD and CMM. Compared with healthy weight, the HR (95%CI) of severe obesity for transition from health to FCMD and from FCMD to CMM was 3.12 (2.91, 3.34) and 1.92 (1.60, 2.31), respectively. On the other hand, underweight was consistently associated with higher mortality risk regardless of initial status, whereas severe obesity was only related to increased risk for transition from health to death (HR: 1.36; 95%CI: 1.17, 1.56) but not for transition from FCMD (HR: 0.70; 95%CI: 0.57, 0.87) or CMM (HR: 0.80; 95%CI: 0.54, 1.19) to death. Conclusion: Our findings highlighted the importance of maintaining healthy weight for primary and secondary prevention of CMD and reflected the demand for more accurate measurement and comprehensive management of obesity for CMD patients. [ABSTRACT FROM AUTHOR]

With the availability of massive amounts of data from electronic health records and registry databases, incorporating time-varying patient information to improve risk prediction has attracted great attention. To exploit the growing amount of predictor information over time, we develop a unified framework for landmark prediction using survival tree ensembles, where an updated prediction can be performed when new information becomes available. Compared to conventional landmark prediction with fixed landmark times, our methods allow the landmark times to be subject-specific and triggered by an intermediate clinical event. Moreover, the nonparametric approach circumvents the thorny issue of model incompatibility at different landmark times. In our framework, both the longitudinal predictors and the event time outcome are subject to right censoring, and thus existing tree-based approaches cannot be directly applied. To tackle the analytical challenges, we propose a risk-set-based ensemble procedure by averaging martingale estimating equations from individual trees. Extensive simulation studies are conducted to evaluate the performance of our methods. The methods are applied to the Cystic Fibrosis Foundation Patient Registry (CFFPR) data to perform dynamic prediction of lung disease in cystic fibrosis patients and to identify important prognosis factors.

Cost analysis is complicated by the fact that patients acquire infections during their hospital stay, having already spent time at risk without having an infection. Multi-state models (MSM) accounts for this time at risk treating infections as time-dependent exposures from ICU admission.

In clinical studies, multi‐state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times may not be known. Motivated by a real dataset of patients who received stem cell transplants, we considered the setting in which some event times were exactly observed and some were missing. In our setting, there was little information about the time intervals in which the missing event times occurred and missingness depended on the event type, given the analysis model covariates. These additional challenges limited the usefulness of some missing data methods (maximum likelihood, complete case analysis, and inverse probability weighting). We show that multiple imputation (MI) of event times can perform well in this setting. MI is a flexible method that can be used with any complete data analysis model. Through an extensive simulation study, we show that MI by predictive mean matching (PMM), in which sampling is from a set of observed times without reliance on a specific parametric distribution, has little bias when event times are missing at random, conditional on the observed data. Applying PMM separately for each sub‐group of patients with a different pathway through the MSM tends to further reduce bias and improve precision. We recommend MI using PMM methods when performing MSM analysis with Markov models and partially observed event times. [ABSTRACT FROM AUTHOR]

Abstract Aims Cognitive impairment and functional status are both important determinants of poor outcomes in heart failure (HF). However, little is known about how functional status impacts the changes in cognitive status during the disease course. This study aimed to describe the cognitive transitions in patients with HF and assess the relationship of these transitions to functional status, which was assessed by the dependency of activities of daily living (ADL). Methods and results This retrospective cohort study included 1764 patients with an International Classification of Diseases‐10 code of HF (≥65 years, mean age 82.3 ± 7.9 years, 39% male) from a long‐term care and medical insurance database from Nobeoka city, a rural city of south‐western Japan. Cognitive status at baseline and 6, 12, 18, and 24 month time points was collected, and participants were stratified based on ADL status at baseline. Generalized estimating equations and multi‐state modelling were used to examine associations between ADL dependency and cognitive changes/mortality. Transition probabilities were estimated using multi‐state modelling. At baseline, there were 1279 (73%) and 485 (27%) patients with independent and dependent ADL, respectively. In overall patients, 1656 (93.9%) patients had normal/mild cognitive status and 108 (6%) patients had a moderate/severe cognitive status at baseline. The majority [104 (96%) patients] of patients with moderate/severe cognitive status at baseline had dependent ADL. In patients with moderate/severe cognitive status, the number of patients with dependent ADL always outnumbered that of the independent ADL throughout the follow‐up. Multi‐state modelling estimated that patients with dependent ADL and normal/mild cognitive status at baseline had 47% probability of maintaining the same cognitive status at 24 months, while the probability of maintaining the same cognitive status was 86% for those with independent ADL. Patients with normal/mild cognitive status in the dependent ADL group at baseline had a higher risk of experiencing a transition to moderate/severe cognitive status at any time point during 24 months compared with those with independent ADL [hazard ratio 5.24 (95% confidence interval 3.47–7.90)]. Conclusions In older patients with HF, the prevalence of cognitive impairment was always higher for those with reduced functional status. Despite having a normal/mild cognitive status at baseline, patients with dependent ADL are at high risk of experiencing cognitive decline over 24 months with substantially less chance of maintaining their cognitive status. ADL dependency was an important risk factor of cognitive decline in patients with HF.

Multi‐state survival models are used to represent the natural history of a disease, forming the basis of a health technology assessment comparing a novel treatment to current practice. Constructing such models for rare diseases is problematic, since evidence sources are typically much sparser and more heterogeneous. This simulation study investigated different one‐stage and two‐stage approaches to meta‐analyzing individual patient data (IPD) in a multi‐state survival setting when the number and size of studies being meta‐analyzed are small. The objective was to assess methods of different complexity to see when they are accurate, when they are inaccurate and when they struggle to converge due to the sparsity of data. Biologically plausible multi‐state IPD were simulated from study‐ and transition‐specific hazard functions. One‐stage frailty and two‐stage stratified models were estimated, and compared to a base case model that did not account for study heterogeneity. Convergence and the bias/coverage of population‐level transition probabilities to, and lengths of stay in, each state were used to assess model performance. A real‐world application to Duchenne Muscular Dystrophy, a neuromuscular rare disease, was conducted, and a software demonstration is provided. Models not accounting for study heterogeneity were consistently out‐performed by two‐stage models. Frailty models struggled to converge, particularly in scenarios of low heterogeneity, and predictions from models that did converge were also subject to bias. Stratified models may be better suited to meta‐analyzing disparate sources of IPD in rare disease natural history/economic modeling, as they converge more consistently and produce less biased predictions of lengths of stay. [ABSTRACT FROM AUTHOR]

Background: Understanding the trend of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is becoming crucial. Previous studies focused on predicting COVID-19 trends, but few papers have considered models for disease estimation and progression based on large real-world data. Methods: We used de-identified data from 60,938 employees of a major financial institution in Italy with daily COVID-19 status information between 31 March 2020 and 31 August 2021. We consider six statuses: (i) concluded case, (ii) confirmed case, (iii) close con- tact, (iv) possible-probable contact, (v) possible contact, and (vi) no-COVID-19 or infection. We conducted a logistic regression to assess the odds ratio (OR) of transition to confirmed COVID-19 case at each time point. We also fitted a general model for disease progression via the multi-state transition probability model at each time point, with lags of 7 and 15 days. Results: Employment in a branch versus in a central office was the strongest predictor of case or contact status, while no association was detected with gender or age. The geographic prevalence of possible-probable contacts and close contacts was predictive of the subsequent risk of confirmed cases. The status with the highest prob- ability of becoming a confirmed case was concluded case (12%) in April 2020, possible-probable contact (16%) in November 2020, and close contact (4%) in August 2021. The model based on transition probabilities predicted well the rate of confirmed cases observed 7 or 15 days later. Conclusion: Data from industry-based surveillance systems may effectively predict the risk of subsequent infection. [ABSTRACT FROM AUTHOR]

The aim of the paper is to carry out survival analysis of a novel multi-state model on infectious disease considering various levels of severity using semi-Markov processes. Various levels of severity of the disease over time and transitions between these severity levels have been considered. Transition probabilities and expected waiting times are derived. Expressions for mean survival time, expected total time in home isolation, and expected total time in hospital are obtained. The analysis of the proposed model is carried out through numerical computation and plotting several graphs. Important conclusions are drawn. The modelling framework proposed here can be used to model any infectious disease irrespective of disease states. The study will be helpful in designing effective measures to control the infectious disease and selecting the appropriate intervention policies. [ABSTRACT FROM AUTHOR]

Aims: Cognitive impairment and functional status are both important determinants of poor outcomes in heart failure (HF). However, little is known about how functional status impacts the changes in cognitive status during the disease course. This study aimed to describe the cognitive transitions in patients with HF and assess the relationship of these transitions to functional status, which was assessed by the dependency of activities of daily living (ADL). Methods and results: This retrospective cohort study included 1764 patients with an International Classification of Diseases‐10 code of HF (≥65 years, mean age 82.3 ± 7.9 years, 39% male) from a long‐term care and medical insurance database from Nobeoka city, a rural city of south‐western Japan. Cognitive status at baseline and 6, 12, 18, and 24 month time points was collected, and participants were stratified based on ADL status at baseline. Generalized estimating equations and multi‐state modelling were used to examine associations between ADL dependency and cognitive changes/mortality. Transition probabilities were estimated using multi‐state modelling. At baseline, there were 1279 (73%) and 485 (27%) patients with independent and dependent ADL, respectively. In overall patients, 1656 (93.9%) patients had normal/mild cognitive status and 108 (6%) patients had a moderate/severe cognitive status at baseline. The majority [104 (96%) patients] of patients with moderate/severe cognitive status at baseline had dependent ADL. In patients with moderate/severe cognitive status, the number of patients with dependent ADL always outnumbered that of the independent ADL throughout the follow‐up. Multi‐state modelling estimated that patients with dependent ADL and normal/mild cognitive status at baseline had 47% probability of maintaining the same cognitive status at 24 months, while the probability of maintaining the same cognitive status was 86% for those with independent ADL. Patients with normal/mild cognitive status in the dependent ADL group at baseline had a higher risk of experiencing a transition to moderate/severe cognitive status at any time point during 24 months compared with those with independent ADL [hazard ratio 5.24 (95% confidence interval 3.47–7.90)]. Conclusions: In older patients with HF, the prevalence of cognitive impairment was always higher for those with reduced functional status. Despite having a normal/mild cognitive status at baseline, patients with dependent ADL are at high risk of experiencing cognitive decline over 24 months with substantially less chance of maintaining their cognitive status. ADL dependency was an important risk factor of cognitive decline in patients with HF. [ABSTRACT FROM AUTHOR]

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Background: Breast cancer is the most common cancer and the leading cause of cancer-related death among women. However, evidence concerning hematological and biochemical markers influencing the natural history of breast cancer from in situ breast cancer to mortality is limited. Methods: In the UK Biobank cohort, 260,079 women were enrolled during 2006–2010 and were followed up until 2019 to test the 59 hematological and biochemical markers associated with breast cancer risk and mortality. The strengths of these associations were evaluated using the multivariable Cox regression models. To understand the natural history of breast cancer, multi-state survival models were further applied to examine the effects of biomarkers on transitions between different states of breast cancer. Results: Eleven biomarkers were found to be significantly associated with the risk of invasive breast cancer, including mainly inflammatory-related biomarkers and endogenous hormones, while serum testosterone was also associated with the risk of in-situ breast cancer. Among them, C-reactive protein (CRP) was more likely to be associated with invasive breast cancer and its transition to death from breast cancer (HR for the highest quartile = 1.46, 95 % CI = 1.07–1.97), while testosterone and insulin-like growth factor-1 (IGF-1) were more likely to impact the early state of breast cancer development (Testosterone: HR for the highest quartile = 1.31, 95 % CI = 1.12–1.53; IGF-1: HR for the highest quartile = 1.17, 95 % CI = 1.00–1.38). Conclusion: Serum CRP, testosterone, and IGF-1 have different impacts on the transitions of different breast cancer states, confirming the role of chronic inflammation and endogenous hormones in breast cancer progression. This study further highlights the need of closer surveillance for these biomarkers during the breast cancer development course.

Abstract Background Cognitive reserve (CR) has been linked to dementia, yet its influence on the risk of depression and related outcomes remains unknown. We aimed to examine the association of CR with depression and subsequent dementia or death, and to assess the extent to which CR is related to depression-free survival. Methods Within the UK Biobank, 436,232 participants free of depression and dementia were followed. A comprehensive CR indicator (low, moderate, and high) was created using latent class analysis based on information on education, occupation, mentally passive sedentary behavior, social connection, confiding with others, and leisure activities. Depression, dementia, and survival status were ascertained through self-reported medical history and/or linkages to medical records. Data were analyzed using multi-state Markov model and Laplace regression. Results Over a median follow-up of 12.96 years, 16,560 individuals developed depression (including 617 with subsequent dementia) and 28,655 died. In multivariable multi-state models, compared with low CR, high CR was associated with lower risk of depression (hazard ratio 0.53 [95% confidence interval 0.51–0.56]) and lower risk of post-depression dementia (0.55 [0.34–0.88]) or death (0.69 [0.55–0.88]) in middle-aged adults (aged

Semi‐competing risks refer to the time‐to‐event analysis setting, where the occurrence of a non‐terminal event is subject to whether a terminal event has occurred, but not vice versa. Semi‐competing risks arise in a broad range of clinical contexts, including studies of preeclampsia, a condition that may arise during pregnancy and for which delivery is a terminal event. Models that acknowledge semi‐competing risks enable investigation of relationships between covariates and the joint timing of the outcomes, but methods for model selection and prediction of semi‐competing risks in high dimensions are lacking. Moreover, in such settings researchers commonly analyze only a single or composite outcome, losing valuable information and limiting clinical utility—in the obstetric setting, this means ignoring valuable insight into timing of delivery after preeclampsia has onset. To address this gap, we propose a novel penalized estimation framework for frailty‐based illness–death multi‐state modeling of semi‐competing risks. Our approach combines non‐convex and structured fusion penalization, inducing global sparsity as well as parsimony across submodels. We perform estimation and model selection via a pathwise routine for non‐convex optimization, and prove statistical error rate results in this setting. We present a simulation study investigating estimation error and model selection performance, and a comprehensive application of the method to joint risk modeling of preeclampsia and timing of delivery using pregnancy data from an electronic health record. [ABSTRACT FROM AUTHOR]

In many populations, the peak period of incidence of type 1 diabetes (T1D) has been observed to be around 10–14 years of age, coinciding with puberty, but direct evidence of the role of puberty in the development of T1D is limited. We therefore aimed to investigate whether puberty and the timing of its onset are associated with the development of islet autoimmunity (IA) and subsequent progression to T1D. A Finnish population-based cohort of children with HLA-DQB1-conferred susceptibility to T1D was followed from 7 years of age until 15 years of age or until a diagnosis of T1D (n = 6920). T1D-associated autoantibodies and growth were measured at 3- to 12-month intervals, and pubertal onset timing was assessed based on growth. The analyses used a three-state survival model. IA was defined as being either positive for islet cell antibodies plus at least one biochemical autoantibody (ICA + 1) or as being repeatedly positive for at least one biochemical autoantibody (BC1). Depending on the IA definition, either 303 (4.4%, ICA + 1) or 435 (6.3%, BC1) children tested positive for IA by the age of 7 years, and 211 (3.2%, ICA + 1)) or 198 (5.3%, BC1) developed IA during follow-up. A total of 172 (2.5%) individuals developed T1D during follow-up, of whom 169 were positive for IA prior to the clinical diagnosis. Puberty was associated with an increase in the risk of progression to T1D, but only from ICA + 1-defined IA (hazard ratio 1.57; 95% confidence interval 1.14, 2.16), and the timing of pubertal onset did not affect the association. No association between puberty and the risk of IA was detected. In conclusion, puberty may affect the risk of progression but is not a risk factor for IA. [ABSTRACT FROM AUTHOR]

Abstract Background The distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the ‘length of stay’) is a key factor in determining how incident caseloads translate into health system burden. Robust estimation of length of stay in real-time requires the use of survival methods that can account for right-censoring induced by yet unobserved events in patient progression (e.g. discharge, death). In this study, we estimate in real-time the length of stay distributions of hospitalised COVID-19 cases in New South Wales, Australia, comparing estimates between a period where Delta was the dominant variant and a subsequent period where Omicron was dominant. Methods Using data on the hospital stays of 19,574 individuals who tested positive to COVID-19 prior to admission, we performed a competing-risk survival analysis of COVID-19 clinical progression. Results During the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0–39, 40–69 and 70 +, respectively, 2.16 (95% CI: 2.12–2.21), 3.93 (95% CI: 3.78–4.07) and 7.61 days (95% CI: 7.31–8.01), compared to 3.60 (95% CI: 3.48–3.81), 5.78 (95% CI: 5.59–5.99) and 12.31 days (95% CI: 11.75–12.95) across the preceding Delta epidemic (1 July 2021–15 December 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95% CI: 1.80–2.30), 2.92 (95% CI: 2.50–3.67) and 6.02 days (95% CI: 4.91–7.01) for the same age groups. Conclusions Hospital length of stay was substantially reduced across all clinical pathways during a mixed Omicron-Delta epidemic compared to a prior Delta epidemic, contributing to a lessened health system burden despite a greatly increased infection burden. Our results demonstrate the utility of survival analysis in producing real-time estimates of hospital length of stay for assisting in situational assessment and planning of the COVID-19 response.

ObjectiveTo evaluate the risk of Alzheimer's disease-related neuropathology burden at autopsy given older adults' current cognitive state.MethodParticipants included 1,303 individuals who enrolled in the Religious Orders Study (ROS) and 1,789 who enrolled in the Rush Memory and Aging Project (MAP). Cognitive status was evaluated via standardized assessments of global cognition and episodic memory. At the time of analyses, about 50% of participants were deceased with the remaining numbers right censored. Using multi-state Cox proportional hazard models, we compared the cognitive status of all subjects alive at a given age and estimated future risk of dying with different AD-related neuropathologies. Endpoints considered were Braak Stages (0-2, 3-4, 5-6), CERAD (0, 1, 2, 3), and TDP-43 (0, 1, 2, 3) level.ResultsFor all three pathological groupings (Braak, CERAD, TDP-43), we found that a cognitive test score one standard deviation below average put individuals at up to three times the risk for being diagnosed with late stage AD at autopsy according to pathological designations. The effect remained significant after adjusting for sex, APOE-e4 status, smoking status, education level, and vascular health scores.ConclusionApplying multi-state modeling techniques, we were able to identify those at risk of exhibiting specific levels of neuropathology based on current cognitive test performance. This approach presents new and approachable possibilities in clinical settings for diagnosis and treatment development programs.

Abstract Background Though the association between air pollution and incident type 2 diabetes (T2D) has been well documented, evidence on the association with development of subsequent diabetes complications and post-diabetes mortality is scarce. We investigate whether air pollution is associated with different progressions and outcomes of T2D. Methods Based on the UK Biobank, 398,993 participants free of diabetes and diabetes-related events at recruitment were included in this analysis. Exposures to particulate matter with a diameter ≤ 10 μm (PM10), PM2.5, nitrogen oxides (NOx), and NO2 for each transition stage were estimated at each participant’s residential addresses using data from the UK’s Department for Environment, Food and Rural Affairs. The outcomes were incident T2D, diabetes complications (diabetic kidney disease, diabetic eye disease, diabetic neuropathy disease, peripheral vascular disease, cardiovascular events, and metabolic events), all-cause mortality, and cause-specific mortality. Multi-state model was used to analyze the impact of air pollution on different progressions of T2D. Cumulative transition probabilities of different stages of T2D under different air pollution levels were estimated. Results During the 12-year follow-up, 13,393 incident T2D patients were identified, of whom, 3791 developed diabetes complications and 1335 died. We observed that air pollution was associated with different progression stages of T2D with different magnitudes. In a multivariate model, the hazard ratios [95% confidence interval (CI)] per interquartile range elevation in PM2.5 were 1.63 (1.59, 1.67) and 1.08 (1.03, 1.13) for transitions from healthy to T2D and from T2D to complications, and 1.50 (1.47, 1.53), 1.49 (1.36, 1.64), and 1.54 (1.35, 1.76) for mortality risk from baseline, T2D, and diabetes complications, respectively. Generally, we observed stronger estimates of four air pollutants on transition from baseline to incident T2D than those on other transitions. Moreover, we found significant associations between four air pollutants and mortality risk due to cancer and cardiovascular diseases from T2D or diabetes complications. The cumulative transition probability was generally higher among those with higher levels of air pollution exposure. Conclusions This study indicates that ambient air pollution exposure may contribute to increased risk of incidence and progressions of T2D, but to diverse extents for different progressions.

Multi-state models are used to describe how individuals transition through different states over time. The distribution of the time spent in different states, referred to as 'length of stay', is often of interest. Methods for estimating expected length of stay in a given state are well established. The focus of this paper is on the distribution of the time spent in different states conditional on the complete pathway taken through the states, which we call 'conditional length of stay'. This work is motivated by questions about length of stay in hospital wards and intensive care units among patients hospitalised due to Covid-19. Conditional length of stay estimates are useful as a way of summarising individuals' transitions through the multi-state model, and also as inputs to mathematical models used in planning hospital capacity requirements. We describe non-parametric methods for estimating conditional length of stay distributions in a multi-state model in the presence of censoring, including conditional expected length of stay (CELOS). Methods are described for an illness-death model and then for the more complex motivating example. The methods are assessed using a simulation study and shown to give unbiased estimates of CELOS, whereas naive estimates of CELOS based on empirical averages are biased in the presence of censoring. The methods are applied to estimate conditional length of stay distributions for individuals hospitalised due to Covid-19 in the UK, using data on 42,980 individuals hospitalised from March to July 2020 from the COVID19 Clinical Information Network. [ABSTRACT FROM AUTHOR]

The analysis of recurrent events in the presence of terminal events requires special attention. Several approaches have been suggested for such analyses either using intensity models or marginal models. When analysing treatment effects on recurrent events in controlled trials, special attention should be paid to competing deaths and their impact on interpretation. This paper proposes a method that formulates a marginal model for recurrent events and terminal events simultaneously. Estimation is based on pseudo-observations for both the expected number of events and survival probabilities. Various relevant hypothesis tests in the framework are explored. Theoretical derivations and simulation studies are conducted to investigate the behaviour of the method. The method is applied to two real data examples. The bivariate marginal pseudo-observation model carries the strength of a two-dimensional modelling procedure and performs well in comparison with available models. Finally, an extension to a three-dimensional model, which decomposes the terminal event per death cause, is proposed and exemplified. [ABSTRACT FROM AUTHOR]

Population-based estimates of excess length of stay after hospital-acquired bacteraemia (HAB) are few and prone to time-dependent bias. We investigated the excess length of stay and readmission after HAB. This population-based cohort study included the North Denmark Region adult population hospitalized for ≥48 hours, from 2006 to 2018. Using a multi-state model with 45 days of follow-up, we estimated adjusted hazard ratios (aHRs) for end of stay and discharge alive. The excess length of stay was defined as the difference in residual length of stay between infected and uninfected patients, estimated using a non-parametric approach with HAB as time-dependent exposure. Confounder effects were estimated using pseudo-value regression. Readmission after HAB was investigated using the Cox regression. We identified 3457 episodes of HAB in 484 291 admissions in 205 962 unique patients. Following HAB, excess length of stay was 6.6 days (95% CI, 6.2–7.1 days) compared with patients at risk. HAB was associated with decreased probability of end of hospital stay (aHR, 0.60; 95% CI, 0.57–0.62) driven by the decreased hazard for discharge alive; the aHRs ranged from 0.30 (95% CI, 0.23–0.40) for bacteraemia stemming from 'heart and vascular' source to 0.72 (95% CI, 0.69–0.82) for the 'urinary tract'. Despite increased post-discharge mortality (aHR, 2.76; 95% CI, 2.38–3.21), HAB was associated with readmission (aHR, 1.42; 95% CI, 1.31–1.53). HAB was associated with considerably excess length of hospital stay compared with hospitalized patients without bacteraemia. Among patients discharged alive, HAB was associated with increased readmission rates. [ABSTRACT FROM AUTHOR]

ObjectiveSystemic arterial hypertension (HT) is a major modifiable risk factor for cardiovascular disease (CVDs), associated with all-cause death (ACD). Understanding its progression from the early state to late complications should lead to more timely intensification of treatment. This study aimed to construct a real-world cohort profile of HT and to estimate transition probabilities from the uncomplicated state to any of these long-term complications; chronic kidney disease (CKD), coronary artery disease (CAD), stroke, and ACD.MethodsThis real-world cohort study used routine clinical practice data for all adult patients diagnosed with HT in the Ramathibodi Hospital, Thailand from 2010 to 2022. A multi-state model was developed based on the following: state 1-uncomplicated HT, 2-CKD, 3-CAD, 4-stroke, and 5-ACD. Transition probabilities were estimated using Kaplan-Meier method.ResultsA total of 144,149 patients were initially classified as having uncomplicated HT. The transition probabilities (95% CI) from the initial state to CKD, CAD, stroke, and ACD at 10-years were 19.6% (19.3%, 20.0%), 18.2% (17.9%, 18.6%), 7.4% (7.1%, 7.6%), and 1.7% (1.5%, 1.8%), respectively. Once in the intermediate-states of CKD, CAD, and stroke, 10-year transition probabilities to death were 7.5% (6.8%, 8.4%), 9.0% (8.2%, 9.9%), and 10.8% (9.3%, 12.5%).ConclusionsIn this 13-year cohort, CKD was observed as the most common complication, followed by CAD and stroke. Among these, stroke carried the highest risk of ACD, followed by CAD and CKD. These findings provide improved understanding of disease progression to guide appropriate prevention measures. Further investigations of prognostic factors and treatment effectiveness are warranted.

Abstract Background Pulmonary embolism (PE) without overt deep vein thrombosis (DVT) was common in hospitalized coronavirus-induced disease (COVID)-19 patients and represented a diagnostic, prognostic, and therapeutic challenge. The aim of this study was to analyze the prognostic role of PE on mortality and the preventive effect of heparin on PE and mortality in unvaccinated COVID-19 patients without overt DVT. Methods Data from 401 unvaccinated patients (age 68 ± 13 years, 33% females) consecutively admitted to the intensive care unit or the medical ward were included in a retrospective longitudinal study. PE was documented by computed tomography scan and DVT by compressive venous ultrasound. The effect of PE diagnosis and any heparin use on in-hospital death (primary outcome) was analyzed by a classical survival model. The preventive effect of heparin on either PE diagnosis or in-hospital death (secondary outcome) was analyzed by a multi-state model after having reclassified patients who started heparin after PE diagnosis as not treated. Results Median follow-up time was 8 days (range 1–40 days). PE cumulative incidence and in-hospital mortality were 27% and 20%, respectively. PE was predicted by increased D-dimer levels and COVID-19 severity. Independent predictors of in-hospital death were age (hazards ratio (HR) 1.05, 95% confidence interval (CI) 1.03–1.08, p

Background: The distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the 'length of stay') is a key factor in determining how incident caseloads translate into health system burden. Robust estimation of length of stay in real-time requires the use of survival methods that can account for right-censoring induced by yet unobserved events in patient progression (e.g. discharge, death). In this study, we estimate in real-time the length of stay distributions of hospitalised COVID-19 cases in New South Wales, Australia, comparing estimates between a period where Delta was the dominant variant and a subsequent period where Omicron was dominant. Methods: Using data on the hospital stays of 19,574 individuals who tested positive to COVID-19 prior to admission, we performed a competing-risk survival analysis of COVID-19 clinical progression. Results: During the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0–39, 40–69 and 70 +, respectively, 2.16 (95% CI: 2.12–2.21), 3.93 (95% CI: 3.78–4.07) and 7.61 days (95% CI: 7.31–8.01), compared to 3.60 (95% CI: 3.48–3.81), 5.78 (95% CI: 5.59–5.99) and 12.31 days (95% CI: 11.75–12.95) across the preceding Delta epidemic (1 July 2021–15 December 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95% CI: 1.80–2.30), 2.92 (95% CI: 2.50–3.67) and 6.02 days (95% CI: 4.91–7.01) for the same age groups. Conclusions: Hospital length of stay was substantially reduced across all clinical pathways during a mixed Omicron-Delta epidemic compared to a prior Delta epidemic, contributing to a lessened health system burden despite a greatly increased infection burden. Our results demonstrate the utility of survival analysis in producing real-time estimates of hospital length of stay for assisting in situational assessment and planning of the COVID-19 response. [ABSTRACT FROM AUTHOR]

• Individuals with higher reserve showed a reduced risk of cognitive decline. • Reserves defined by the residual method were strong predictors of cognitive change. • The estimated stage duration from preclinical to mild AD dementia varied by reserve. The relationship of cognitive reserve and measures of reserve with longitudinal cognitive change and the duration of preclinical, prodromal, and mild Alzheimer disease (AD) dementia remains to be fully characterized. In our study, 660 β-amyloid-positive participants staged with preclinical AD, prodromal AD, and dementia due to AD from the Alzheimer's Disease Neuroimaging Initiative were selected. Cognitive reserve and brain reserve were defined by conventional proxies or the residual method at baseline. We evaluated the utility of these reserves in predicting longitudinal cognitive change by mixed effects models and used a multi-state model to estimate stage duration stratified by reserve groups. Corrected for age, sex, and APOE-ε4 status, reserve was associated with cognitive decline, and the effects changed depending on the specific measures of reserve and the stage of AD. Reserves defined by the residual method were stronger predictors of cognitive change than those defined by conventional proxies. The estimated time from preclinical to mild AD dementia varied from 15–24 years based on the different reserve groups, and we observed a linear trend for the longest duration in individuals with high cognitive reserve/high brain reserve, followed by those with high cognitive reserve/low brain reserve, low cognitive reserve/high brain reserve, and low cognitive reserve/low brain reserve. This study showed a reduced risk of cognitive decline for individuals with higher level of reserve regardless of methods for measuring reserve. Interindividual differences in reserve may be important for clinical practice and trial design. [ABSTRACT FROM AUTHOR]

Abstract Clofarabine is an active antileukemic drug for subgroups of patients with acute myeloid leukemia (AML). Multi‐state models can provide additional insights to supplement the original intention‐to‐treat analysis of randomized controlled trials (RCT). We re‐analyzed the HOVON102/SAKK30/09 phase III RCT for newly diagnosed AML patients, which randomized between standard induction chemotherapy with or without clofarabine. Using multi‐state models, we evaluated the effects of induction chemotherapy outcomes (complete remission [CR], measurable residual disease [MRD]), and post‐remission therapy with allogeneic stem cell transplantation [alloSCT] on relapse and death. Through the latter a consistent reduction in the hazard of relapse in the clofarabine arm compared to the standard arm was found, which occurred irrespective of MRD status or post‐remission treatment with alloSCT, demonstrating a strong and persistent antileukemic effect of clofarabine. During the time period between achieving CR and possible post‐remission treatment with alloSCT, non‐relapse mortality was higher in patients receiving clofarabine. An overall net benefit of treatment with clofarabine was identified using the composite endpoint current leukemia‐free survival (CLFS). In conclusion, these results enforce and extend the earlier reported beneficial effect of clofarabine in AML and show that multi‐state models further detail the effect of treatment on competing and series of events.

• Development of generalized probabilistic model for wind speed intermittency by utilizing real-time acquired cloud-based IoT wind speed data for wind energy system in a SA paradigm. • Simplified reliability analysis through multi-state modeling and probability distribution function for wind-integrated systems, departing from conventional convolution-based approaches. • Forecasting of wind speed and wind power generation for two days in advance through a time-series non-linear autoregressive model with exogenous inputs (NARX), and estimation of the operational reliability using the forecasted wind speed and wind power data. The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations. [ABSTRACT FROM AUTHOR]

• Advanced situational awareness for operational reliability for wind energy systems (WES). • Comprehensive uncertainty analysis of wind speed and the development of a multi-state model for accurately capturing the variability of wind generator power output. • Time-series forecasting of wind speed and power generation for two days ahead employing a non-linear autoregressive with exogenous input ANN (NARX-ANN) model to project operational reliability. • Implementation of an IoT-based real-time monitoring framework to enhance the projection and management of WES operational reliability. Wind power fluctuations impact power system flexibility and reliability, emphasizing situational awareness (SA) in wind power integration. Monitoring wind speed and power generation forms the core of SA, enabling insight into wind system conditions. An Integrated Development Environment (IDE) becomes pivotal in assessing the SA of wind-integrated systems, which is vital for forecasting operational reliability in power system planning. The IDE facilitates Simulink-based wind model development and is linked to dSPACE hardware through a real-time interface (RTI) for validation. This IDE-driven integration utilizes cloud-based Internet of Things (IoT) capabilities, acquiring wind speed and power generation data for precise SA evaluations. A multi-state wind speed model is employed to incorporate energy generation fluctuations. To improve this, the artificial neural network (ANN) model, i.e., a time series-based non-linear autoregressive with exogenous input (NARX) ANN, is utilized to predict future wind speed and power generation, strengthening SA-based operational reliability. The study investigates the effectiveness of a suggested operational planning strategy under wind generation uncertainties. This assessment is carried out within a real-time deployment of wind energy systems seamlessly integrated with IoT on the dSPACE platform. The primary objective is to evaluate the system's ability to maintain SA effectively. [ABSTRACT FROM AUTHOR]

With the growing interdependence between the electricity system and the natural gas system, the operation uncertainties in either subsystem, such as wind fluctuations or component failures, could have a magnified impact on the reliability of the whole system due to energy interactions. A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system (IEGS). Therefore, this paper proposes a day-ahead security-constrained unit commitment (SCUC) model for the IEGS to schedule the operation and reserve si-multaneously considering reliability requirements. Firstly, the multi-state models for generating units and gas wells are established. Based on the multi-state models, the expected unserved energy cost (EUEC) and the expected wind curtailment cost (EWC) criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS. Furthermore, the EUEC and EWC criteria are incorporated into the day-ahead SCUC model, which is noncon-vex and mathematically reformulated into a solvable mixed-integer second-order cone programming (MISOCP) problem. The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system. Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.

Background: The multi-state models help more closely study of the factors affecting the survival of patients with breast cancer. Method: We conducted the present retrospective cohort study on 2030 Iranian patients with breast cancer in 2020. The patients’ follow-up period ranged from 1 month to 15 years. Accordingly, the initial treatment, metastasis, and death were considered as the first, second, and absorbing states, respectively. The multi-state model was utilized for modeling and analyzing the data at a 95% significance level using the MSM package in R software. Results: The mean age (± standard deviation) of the patients included at diagnosis time was 55.3 (±12.07) years old. The first one year and 5 years adjusted transition probabilities for transitions from the treatment to metastasis estimated as 0.85 (0.15 – 0.89) and 0.45 (0.21 – 0.61), and for metastasis to death transitions, they were estimated as 0.15 (0.1 – 0.21) and 0.55 (0.41 - 0.69), respectively. Moreover, the average sojourn times were estimated as 0.27 and 74.85 months for the treatment and metastasis states, respectively. Conclusion: The obtained results revealed that over time, the transition probabilities of patients from surgery to metastasis state decreased, whereas the transition probabilities from metastasis to death state increased using the multi-state model.

Background: Though the association between air pollution and incident type 2 diabetes (T2D) has been well documented, evidence on the association with development of subsequent diabetes complications and post-diabetes mortality is scarce. We investigate whether air pollution is associated with different progressions and outcomes of T2D.Methods: Based on the UK Biobank, 398,993 participants free of diabetes and diabetes-related events at recruitment were included in this analysis. Exposures to particulate matter with a diameter ≤ 10 μm (PM10), PM2.5, nitrogen oxides (NOx), and NO2 for each transition stage were estimated at each participant's residential addresses using data from the UK's Department for Environment, Food and Rural Affairs. The outcomes were incident T2D, diabetes complications (diabetic kidney disease, diabetic eye disease, diabetic neuropathy disease, peripheral vascular disease, cardiovascular events, and metabolic events), all-cause mortality, and cause-specific mortality. Multi-state model was used to analyze the impact of air pollution on different progressions of T2D. Cumulative transition probabilities of different stages of T2D under different air pollution levels were estimated.Results: During the 12-year follow-up, 13,393 incident T2D patients were identified, of whom, 3791 developed diabetes complications and 1335 died. We observed that air pollution was associated with different progression stages of T2D with different magnitudes. In a multivariate model, the hazard ratios [95% confidence interval (CI)] per interquartile range elevation in PM2.5 were 1.63 (1.59, 1.67) and 1.08 (1.03, 1.13) for transitions from healthy to T2D and from T2D to complications, and 1.50 (1.47, 1.53), 1.49 (1.36, 1.64), and 1.54 (1.35, 1.76) for mortality risk from baseline, T2D, and diabetes complications, respectively. Generally, we observed stronger estimates of four air pollutants on transition from baseline to incident T2D than those on other transitions. Moreover, we found significant associations between four air pollutants and mortality risk due to cancer and cardiovascular diseases from T2D or diabetes complications. The cumulative transition probability was generally higher among those with higher levels of air pollution exposure.Conclusions: This study indicates that ambient air pollution exposure may contribute to increased risk of incidence and progressions of T2D, but to diverse extents for different progressions. [ABSTRACT FROM AUTHOR]

Multi-state models are frequently used when data come from subjects observed over time and where focus is on the occurrence of events that the subjects may experience. A convenient modeling assumption is that the multi-state stochastic process is Markovian, in which case a number of methods are available when doing inference for both transition intensities and transition probabilities. The Markov assumption, however, is quite strict and may not fit actual data in a satisfactory way. Therefore, inference methods for non-Markov models are needed. In this paper, we review methods for estimating transition probabilities in such models and suggest ways of doing regression analysis based on pseudo observations. In particular, we will compare methods using land-marking with methods using plug-in. The methods are illustrated using simulations and practical examples from medical research. [ABSTRACT FROM AUTHOR]

One major goal in clinical applications of multi-state models is the estimation of transition probabilities. Estimators based on subsampling were recently introduced by de Uña-Álvarez and Meira-Machado to estimate these quantities, and their superiority with respect to the competing estimators has been proved in situations in which the Markov condition is violated. The idea behind the proposed estimators is to use a procedure based on (differences between) Kaplan-Meier estimators derived from a subset of the data consisting of all subjects observed to be in a given state at a given time. Subsampling, also referred to as landmarking, leads to small sample sizes and high percentage of censoring for which more efficient estimators are essential. Preliminary smoothing, also known as presmoothing, is a good alternative to these situations. The presmoothed estimators are obtained by replacing the censoring indicator variables in the classical definitions by values of a regression estimator. The behavior of the presmoothed landmark estimators for the transition probabilities is explored through simulation studies when different ways to estimate the conditional probability of uncensoring are used. Real data illustration is included. [ABSTRACT FROM AUTHOR]

To optimize colorectal cancer (CRC) surveillance, accurate information on the risk of developing CRC from premalignant lesions is essential. However, directly observing this risk is challenging since precursor lesions, i.e., advanced adenomas (AAs), are removed upon detection. Statistical methods for multistate models can estimate risks, but estimation is challenging due to low CRC incidence. We propose an outcome-dependent sampling (ODS) design for this problem in which we oversample CRCs. More specifically, we propose a three-state model for jointly estimating the time distributions from baseline colonoscopy to AA and from AA onset to CRC accounting for the ODS design using a weighted likelihood approach. We applied the methodology to a sample from a Norwegian adenoma cohort (1993-2007), comprising 1, 495 individuals (median follow-up 6.8 years [IQR: 1.1 - 12.8 years]) of whom 648 did and 847 did not develop CRC. We observed a 5-year AA risk of 13% and 34% for individuals having non-advanced adenoma (NAA) and AA removed at baseline colonoscopy, respectively. Upon AA development, the subsequent risk to develop CRC in 5 years was 17% and age-dependent. These estimates provide a basis for optimizing surveillance intensity and determining the optimal trade-off between CRC prevention, costs, and use of colonoscopy resources., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objectives: We used a multi-state model, which mitigates time-dependent bias, to estimate the mortality, length of stay (LOS), and costs of methicillin-resistant Staphylococcus aureus (MRSA) infections in Singapore., Methods: We conducted a retrospective study in a hospital in Singapore from 2018 to 2022. Patients with MRSA infections were matched 1:1:3 to patients with MRSA colonization and patients without MRSA by age, gender, specialty, and intensive care admission, respectively. A multi-state model was used to derive excess LOS and mortality hazard ratios. The attributable cost of infections was estimated in 2022 Singapore dollars (SGDs) from the health care perspective., Results: We matched 536 patients with MRSA infections to 536 patients with MRSA colonization, and to 1608 patients without MRSA. The excess LOS due to MRSA infection was 2.11 (95% confidence interval [CI] 2.05-2.17) days compared with MRSA colonization and 3.75 (95% CI 3.69-3.80) days compared with no MRSA, which translated to an excess cost of SGD $1825 and SGD $3238, respectively. Of the different MRSA infection types, pneumonia had the highest mortality risk (hazard ratio 4.13; 95% CI 2.28-7.50) compared with patients without MRSA., Conclusions: MRSA infections increased hospital LOS and health care costs in Singapore. Our estimates can inform future economic analyses of management strategies against MRSA., Competing Interests: The authors have no competing interests to declare., (© 2024 The Author(s).)

Abstract Background Understanding the risk factors associated with hospital burden of COVID-19 is crucial for healthcare planning for any future waves of infection. Methods An observational cohort study is performed, using data on all PCR-confirmed cases of COVID-19 in Regione Lombardia, Italy, during the first wave of infection from February-June 2020. A multi-state modelling approach is used to simultaneously estimate risks of progression through hospital to final outcomes of either death or discharge, by pathway (via critical care or not) and the times to final events (lengths of stay). Logistic and time-to-event regressions are used to quantify the association of patient and population characteristics with the risks of hospital outcomes and lengths of stay respectively. Results Risks of severe outcomes such as ICU admission and mortality have decreased with month of admission (for example, the odds ratio of ICU admission in June vs March is 0.247 [0.120–0.508]) and increased with age (odds ratio of ICU admission in 45–65 vs 65 + age group is 0.286 [0.201–0.406]). Care home residents aged 65 + are associated with increased risk of hospital mortality and decreased risk of ICU admission. Being a healthcare worker appears to have a protective association with mortality risk (odds ratio of ICU mortality is 0.254 [0.143–0.453] relative to non-healthcare workers) and length of stay. Lengths of stay decrease with month of admission for survivors, but do not appear to vary with month for non-survivors. Conclusions Improvements in clinical knowledge, treatment, patient and hospital management and public health surveillance, together with the waning of the first wave after the first lockdown, are hypothesised to have contributed to the reduced risks and lengths of stay over time.

Background: Pulmonary embolism (PE) without overt deep vein thrombosis (DVT) was common in hospitalized coronavirus-induced disease (COVID)-19 patients and represented a diagnostic, prognostic, and therapeutic challenge. The aim of this study was to analyze the prognostic role of PE on mortality and the preventive effect of heparin on PE and mortality in unvaccinated COVID-19 patients without overt DVT. Methods: Data from 401 unvaccinated patients (age 68 ± 13 years, 33% females) consecutively admitted to the intensive care unit or the medical ward were included in a retrospective longitudinal study. PE was documented by computed tomography scan and DVT by compressive venous ultrasound. The effect of PE diagnosis and any heparin use on in-hospital death (primary outcome) was analyzed by a classical survival model. The preventive effect of heparin on either PE diagnosis or in-hospital death (secondary outcome) was analyzed by a multi-state model after having reclassified patients who started heparin after PE diagnosis as not treated. Results: Median follow-up time was 8 days (range 1–40 days). PE cumulative incidence and in-hospital mortality were 27% and 20%, respectively. PE was predicted by increased D-dimer levels and COVID-19 severity. Independent predictors of in-hospital death were age (hazards ratio (HR) 1.05, 95% confidence interval (CI) 1.03–1.08, p < 0.001), body mass index (HR 0.93, 95% CI 0.89–0.98, p = 0.004), COVID-19 severity (severe versus mild/moderate HR 3.67, 95% CI 1.30–10.4, p = 0.014, critical versus mild/moderate HR 12.1, 95% CI 4.57–32.2, p < 0.001), active neoplasia (HR 2.58, 95% CI 1.48–4.50, p < 0.001), chronic obstructive pulmonary disease (HR 2.47; 95% CI 1.15–5.27, p = 0.020), respiratory rate (HR 1.06, 95% CI 1.02–1.11, p = 0.008), heart rate (HR 1.03, 95% CI 1.01–1.04, p < 0.001), and any heparin treatment (HR 0.35, 95% CI 0.18–0.67, p = 0.001). In the multi-state model, preventive heparin at prophylactic or intermediate/therapeutic dose, compared with no treatment, reduced PE risk and in-hospital death, but it did not influence mortality of patients with a PE diagnosis. Conclusions: PE was common during the first waves pandemic in unvaccinated patients, but it was not a negative prognostic factor for in-hospital death. Heparin treatment at any dose prevented mortality independently of PE diagnosis, D-dimer levels, and disease severity. [ABSTRACT FROM AUTHOR]

Multi-state models provide an extension of the usual survival/event-history analysis setting. In the medical domain, multi-state models give the possibility of further investigating intermediate events such as relapse and remission. In this work, a further extension is proposed using relative survival, where mortality due to population causes (i.e. non-disease-related mortality) is evaluated. The objective is to split all mortality in disease and non-disease-related mortality, with and without intermediate events, in datasets where cause of death is not recorded or is uncertain. To this end, population mortality tables are integrated into the estimation process, while using the basic relative survival idea that the overall mortality hazard can be written as a sum of a population and an excess part. Hence, we propose an upgraded non-parametric approach to estimation, where population mortality is taken into account. Precise definitions and suitable estimators are given for both the transition hazards and probabilities. Variance estimating techniques and confidence intervals are introduced and the behaviour of the new method is investigated through simulations. The newly developed methodology is illustrated by the analysis of a cohort of patients followed after an allogeneic hematopoietic stem cell transplantation. The work has been implemented in the R package mstate. [ABSTRACT FROM AUTHOR]

This article discusses the spread of infectious diseases using the multi-state SVIRS model with the assumption that a discrete-time Markov chain (DTMC) occurs in a closed population that is regularly examined. This article aims to generate transition probabilities, which are then used to predict the number of confirmed cases in the next period. The multi-state SVIRS model uses four states, namely susceptible, vaccinated, infected, and recovered, followed by calculating the probabilities of each transition between states that are different from the compartment model. The model was applied to the COVID-19 data in Indonesia, which was analyzed using the statistical software R. The result showed that the transition probability of a person being infected according to the multistate model with the assumption of DTMC SVIRS on the COVID-19 data was around 25.38% including those with and without vaccination. In comparison, the probability of being recovered was about 92.34%. Then this transition probability was used to predict the confirmed cases of COVID-19 in the next few days. The prediction results were highly accurate with a MAPE value less than 10%. The main contribution of this research is the use of the DTMC assumption, which is a stochastic model in determining the parameters of the differential equation formed by the compartment model and adding the vaccinated state in the model. The vaccinated cases in this article used the proportion of the efficacy of each vaccine used by several susceptible individuals, which, according to WHO recommendations, should be given in two doses. The multi-state model with the assumption of DTMC can model chronic diseases and infectious diseases. This can be seen from the results of the analysis of the COVID-19 data in Indonesia, in which the short-term prediction results had a high level of accuracy. [ABSTRACT FROM AUTHOR]

Purpose: To identify parameters that can predict the postoperative risk for progression of retinopathy to a vision‐threatening stage after cataract surgery. This may optimize the timing of surgery and the postoperative follow‐up strategy in diabetic patients. Methods: Multi‐state survival analysis with death as competing risk was used to investigate how year of onset and age of onset of diabetes, gender, body mass index, HbA1c and blood pressure had affected the risk for developing diabetic macular oedema (DME) and proliferative diabetic retinopathy (PDR) among 2540 right eyes from 2797 diabetic patients operated for cataract on one or both eyes during 25 years until July 1. 2019. Results: Cataract surgery had been performed in 98.8% of patients reaching 90 years of age. The risk for developing both DME and PDR was increased by cataract surgery. The risk was highest during the first postoperative years and increased by pre‐operative variability in HbA1c. The risk after more than 20 years postoperatively increased by increased cumulative HbA1c pre‐operatively. The other studied risk factors contributed differently to the development of the two complications. Conclusions: Decision models for the timing of cataract surgery in diabetic patients should consider that the risk for developing vision‐threatening retinopathy depends on follow‐up time. Differences in the risk profiles for developing DME and PDR after cataract surgery support that the two complications should be regarded as separate late complications. [ABSTRACT FROM AUTHOR]

Semi-Markov models are widely used for survival analysis and reliability analysis. In general, there are two competing parameterizations and each entails its own interpretation and inference properties. On the one hand, a semi-Markov process can be defined based on the distribution of sojourn times, often via hazard rates, together with transition probabilities of an embedded Markov chain. On the other hand, intensity transition functions may be used, often referred to as the hazard rates of the semi-Markov process. We summarize and contrast these two parameterizations both from a probabilistic and an inference perspective, and we highlight relationships between the two approaches. In general, the intensity transition based approach allows the likelihood to be split into likelihoods of two-state models having fewer parameters, allowing efficient computation and usage of many survival analysis tools. Nevertheless, in certain cases the sojourn time based approach is natural and has been exploited extensively in applications. In contrasting the two approaches and contemporary relevant R packages used for inference, we use two real datasets highlighting the probabilistic and inference properties of each approach. This analysis is accompanied by an R vignette. [ABSTRACT FROM AUTHOR]