The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models

Associations of ambient air pollution with incidence and dynamic progression of atrial fibrillation

The influence of air pollution on dynamic changes in clinical state from healthy to atrial fibrillation (AF), further AF-related complications and ultimately, death are unclear. We aimed to investigate the relationships between air pollution and the occurrence and progression trajectories of AF. We retrieved 442,150 participants free of heart failure (HF), myocardial infarction (MI), stroke and dementia at baseline from UK Biobank. Exposures to air pollution for each transition stage were estimated at the geocoded residential address of each participant using the bilinear interpolation approach. The outcomes were incident AF, complications, and death. Multi-stage models were used to evaluate the associations between air pollution and dynamic progression of AF. Over a 12.6-year median follow-up, a total of 21,670 incident AF patients were identified, of whom, 4103 developed complications and 1331 died. PM2.5, PM10, NOx and NO2 were differentially positively associated, while O3 was negatively associated with risks of progression trajectories of AF. PM2.5 exposure was significantly associated with an increased risk of progression. The associations of PM2.5, PM10, NOx, and NO2 on incident AF were generally more pronounced compared to other transitions. The cumulative transition probabilities were generally higher in individuals with higher exposure levels of PM2.5, PM10, NOx, and NO2 and lower exposure to O3. Air pollution could potentially have a role in increasing the risk of both the occurrence and progression of AF, emphasizing the significance of air pollution interventions in both the primary prevention of AF and the management of AF-related outcomes.

Association of Short-term Pain and Chronic Pain Intensity With Cardiometabolic Multimorbidity Progression: A Multistate Markov Model Analysis

BACKGROUND

The impact of pain intensity on the progression trajectories of cardiometabolic multimorbidity (CMM) is not well understood. We attempted to dissect the relationship of short-term pain (STP) and chronic pain intensity with the temporal progression of CMM.

METHODS

We conducted a prospective cohort study based on the UK Biobank participants. Incident cases of cardiometabolic diseases (CMDs) were identified based on self-reported information and multiple health-related records in the UK Biobank. CMM was defined as the occurrence of at least 2 CMDs, including heart failure (HF), ischemic heart disease (IHD), stroke, and type 2 diabetes (T2D). The pain intensity was categorized into 5 levels based on pain duration and the number of sites involved, including chronic widespread pain (CWSP), chronic multilocation pain (CMLP), chronic single-location pain (CSLP), STP, and free-of-pain (FOP). Multistate models were used to assess the impact of pain intensity on the CMM trajectories from enrollment to initial cardiometabolic disease (ICMD), subsequently to CMM, and ultimately to death.

RESULTS

A total of 429,145 participants were included. Over the course of a 12.8-year median follow-up, 13.1% (56,137/429,145) developed ICMD, 19.6% (10,979/56,137) further progressed to CMM, and a total of 5.3% (22,775/429,145) died. Compared with FOP, CMLP (hazard ratio [HR], 1.11; 95% confidence interval [CI], 1.06-1.17) and CWSP (HR, 1.26; 95% CI, 1.13-1.42) elevated the risk of transitioning from ICMD to CMM. STP (HR, 0.89; 95% CI, 0.82-0.96), CSLP (HR, 0.88; 95% CI, 0.82-0.95), and CMLP (HR, 0.87; 95% CI, 0.81-0.93) lowered the risk of transition from ICMD to mortality, and STP also reduced the risk of transition from enrollment to mortality (HR, 0.94; 95% CI, 0.89-0.98). The results of disease-specific transitions revealed that the influence of pain intensity varied across transitional stages. Specifically, CMLP and CWSP heightened the risk of conversion from T2D or IHD to CMM, whereas only CWSP substantially elevated the transition risk from HF to CMM.

CONCLUSIONS

Our results highlighted reductions in chronic pain may mitigate both the onset and progression of CMM, potentially having an important impact on future revisions of cardiometabolic and pain-related guidelines.

Socioeconomic disparity in the natural history of cutaneous melanoma: evidence from two large prospective cohorts

BACKGROUND

Previous studies on the associations between socioeconomic status (SES) and cutaneous malignant melanoma (CMM) failed to distinguish the effects of different SES factors under an individual-data-based prospective study design.

METHODS

Based on UK Biobank (UKB) and China Kadoorie Biobank (CKB), we estimated the effects of four SES factors on transitions from baseline to CMM in situ, subsequently to invasive CMM and further CMM mortality by applying multistate models. We further explored to which extent the associations between SES and CMM incidence could be explained by potential mediators including sun exposure, lifestyle and ageing in UKB.

RESULTS

In multistate analyses, good household income was independently associated with an increased risk of CMM in situ (HR=1.38, 95% CI: 1.21 to 1.58) and invasive CMM (HR=1.34, 95% CI: 1.22 to 1.48) in UKB. These findings were partly validated in CKB. Especially in UKB, we observed an increased risk of CMM in situ and invasive CMM among participants with good type of house; only good education was independently associated with lower risk of evolving to invasive CMM among patients with CMM in situ (HR=0.69, 95% CI: 0.52 to 0.92); only good household income was independently associated with lower risk of CMM mortality among patients with CMM (HR=0.65, 95% CI: 0.45 to 0.95). In mediation analysis, the proportions attributable to the mediating effect were <6% for all selected variables, including self-reported sun exposure-related factors.

CONCLUSION

SES factors have different effects on the incidence and progression of CMM. The association between SES and incident CMM is neither causal nor well explained by selected mediators.

The progression trajectory of Bipolar Disorder: results from the application of a staging model over a ten-year observation

BACKGROUND

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.

METHODS

Multistate Model using the mstate package in R and Markov model with stratified hazards were used for statistical analysis.

RESULTS

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.

CONCLUSION

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.

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.

A multistate transition model of changes in loneliness and carer stress among community-dwelling older adults in Aotearoa New Zealand

OBJECTIVES

To identify changes in loneliness and carer stress between two time points for older people of different ethnicities who had repeated interRAI home care assessments.

METHODS

Participants consisted of community-dwelling older adults across New Zealand who received two interRAI-HC assessments between 5 July 2012 and 31 December 2019. Two multistate models were developed: the first model was not lonely versus lonely, and the second model was no carer stress versus carer stress. The one-year transition probabilities were calculated. Mean sojourn times were calculated for each state except death. Paired t-tests assessed the differences in transition probabilities between the different ethnic groups.

RESULTS

The mean age of the cohort was 82.5 years (SD 7.7 years). At first assessment, 14,646 (21%) older people stated they were lonely and 26,789 carers (38%) experienced stress. The most common first transition type was not lonely to not lonely: Māori 42%, Pacific 54%, Asian, 48% and Other 40%. The highest one-year transition probability in the loneliness model was living in aged residential care to death (0.79). The most common first transition type for the carer stress was no carer stress to no carer stress: Māori 35%, Pacific, 46%, Asian, 43% and Other 33%. The highest one-year transition probability in the carer stress model was living in aged residential care to death (0.80). The statuses not lonely and no carer stress had a mean sojourn time of approximately one year, and eight months to one year, respectively.

CONCLUSIONS

Loneliness can change over time due to circumstances and an individual's perception of loneliness at the time of assessment. Carer stress is enduring and has a low probability of improvement.

Courier delivery of antiretroviral therapy: a cohort study of a South African private-sector HIV programme

INTRODUCTION

Courier delivery has become a popular antiretroviral therapy (ART) distribution method in some HIV care settings, yet data on ART courier delivery and how it relates to ART outcomes are scarce. We studied the differences in viral suppression rates between individuals from a South African private sector HIV programme receiving ART by courier delivery and those receiving ART through traditional retail dispensing.

METHODS

Individuals aged 15 years or older who were actively enrolled in the Aid for AIDS programme between January 2011 and July 2022 were eligible for the analysis. The outcome of interest was viral suppression defined as a viral load (VL) <400 copies per ml. We calculated adjusted odds ratios (OR) for the association between the ART distribution method and viral suppression, comparing those receiving refills through courier pharmacies versus retail dispensing at the time of the VL testing. We used generalized estimating equations to account for repeated VL testing of the same individual. The models were adjusted for age, sex, calendar year, ART regimen, history of mental illness and medical insurance scheme. We computed adjusted ORs for the calendar periods 2011-2013, 2014-2016, 2017-2019, 2020-2022 and overall.

RESULTS

We extracted 442,619 VL measurements from 68,720 eligible individuals, 39,406 (57.3%) were women. The median number of VL measurements per individual was 6 (IQR 3-10). VL suppression was detected in 398,901 (90.1%) tests, and 185,701 (42.0%) of the tests were taken while the individual was receiving ART by courier delivery. Overall, courier delivery was associated with 5% higher odds of viral suppression than retail dispensing (adjusted OR 1.05, 95% CI 1.02-1.08). The strength and direction of this association varied by calendar period, with an adjusted OR of 1.37 (95% CI 1.27-1.48) in 2011-2013 and 1.02 (95% CI 0.97-1.07) in 2020-2022.

CONCLUSIONS

Courier delivery of ART is a viable alternative to retail dispensing in the South African private sector, as it was associated with higher viral suppression until 2016 and similar suppression rates in recent years. Further research is needed to investigate the potential benefits and drawbacks of courier delivery of ART in both private and public healthcare settings.

Pediatric hip disorders are not associated with an increased 10-year revision risk after total hip arthroplasty under the age of 55: results from the Dutch Arthroplasty Register

BACKGROUND AND PURPOSE

 Developmental dysplasia (DDH) and Legg-Calvé-Perthes disease (LCPD) are common indications for total hip arthroplasty (THA) at a young age, and may be associated with increased revision risk. We aimed to investigate the 10-year cumulative aseptic cup revision and overall revision risk of THA, and investigated whether these are increased compared with THA for primary osteoarthritis (OA) in patients below 55 years.

METHODS

 All THAs (2007-2019) in patients under the age of 55 for the indications OA, DDH, and LCPD were extracted from the Dutch Arthroplasty register. The 10-year cumulative incidences of aseptic cup failure and overall revision were assessed for the 3 groups, with death as a competing risk. Cox regression analysis was used.

RESULTS

 24,263 THAs were identified: 20,645 (85%) for OA, 3,032 (13%) for DDH, and 586 (2%) for LCPD. The 10-year cumulative revision risk for aseptic cup failure was 3.4% (95% confidence interval [CI] 3.0-3.8) for OA, 3.4% (CI 2.4-3.4) for DDH, and 1.7% (CI 0.2-3.1) for LCPD. The 10-year cumulative overall revision risk was 6.0% (CI 5.6-6.5) for OA, 6.0% (CI 4.9-7.2) for DDH, and 5.1% (2.7-7.5) for LCPD. The multivariable Cox regression analysis for aseptic cup failure yielded hazard ratios of 0.7 (0.5-1.2) for DDH, and 0.8 (0.3-2.1) for LCPD compared with OA. No statistically significant differences for overall revision were found.

CONCLUSION

 THA performed for DDH or LCDP in patients under the age of 55 was not associated with a statistically significant increased risk of aseptic cup revision or overall revision, compared with THA performed for primary OA in the same age group.

Role of lifestyle factors on the development and long-term prognosis of pneumonia and cardiovascular disease in the Chinese population

BACKGROUND

Whether adherence to a healthy lifestyle is associated with a lower risk of developing pneumonia and a better long-term prognosis remains unclear. This study aimed to investigate associations of individual and combined lifestyle factors (LFs) with the incidence risk and long-term prognosis of pneumonia hospitalization.

METHODS

Using data from the China Kadoorie Biobank study, we used the multistate models to investigate the role of five high-risk LFs, including smoking, excessive alcohol drinking, unhealthy dietary habits, physical inactivity, and unhealthy body shape, alone or in combination in the transitions from a generally healthy state at baseline to pneumonia hospitalization or cardiovascular disease (CVD, regarded as a reference outcome), and subsequently to mortality.

RESULTS

Most of the five high-risk LFs were associated with increased risks of transitions from baseline to pneumonia and from pneumonia to death, but with different risk estimates. The greater the number of high-risk LFs, the higher the risk of developing pneumonia and long-term mortality risk after pneumonia, with the strength of associations comparable to that of LFs and CVD. Compared to participants with 0-1 high-risk LF, the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for transitions from baseline to pneumonia and from pneumonia to death in those with five high-risk LFs were 1.43 (1.28-1.60) and 1.98 (1.61-2.42), respectively. Correspondingly, the respective HRs (95% CIs) for transitions from baseline to CVD and from CVD to death were 2.00 (1.89-2.11) and 1.44 (1.30-1.59), respectively. The risk estimates changed slightly when further adjusting for the presence of major chronic diseases.

CONCLUSION

In this Chinese population, unhealthy LFs were associated with an increased incidence and long-term mortality risk of pneumonia.

Lifestyle, air pollution, and risk of multimorbidity in a prospective analysis of the UK Biobank cohort

Background

Although associations between chronic obstructive pulmonary disease (COPD) or ischaemic heart disease (IHD) and lifestyle factors or air pollution factors (referred as LAFs below) are well-established, it is unclear the influences of LAFs on the trajectory of IHD and COPD multimorbidity (referred as ICM below). Therefore, this study investigated the influences of LAFs on the trajectory of ICM from healthy to IHD or COPD, to ICM, and to all-cause death.

Methods

A cohort of 339,213 participants from the UK Biobank aged 37-73 who were free of IHD and COPD were included. A multi-state model was used to analyse the influences of high-risk factors including current smoking or quitting due to illness or physician's advice, current excessive alcohol drinking, physical inactivity, unhealthy body shape, and excessive air pollution with particulates matter with an aerodynamic diameter ≤2.5 μm (PM2.5) on ICM trajectory.

Results

During a median follow-up of 13.74 years, 46,398 participants developed IHD or COPD (referred as IOC below), 3949 developed ICM, and 35,691 died from any cause. All five high-risk factors played crucial but different roles in these transitions. The hazard ratios (95 % confidence intervals) per one-factor increase were 1.29 (1.27-1.3), 1.38 (1.33-1.44), and 1.69 (1.56-1.84) for transitions from baseline to IOC, from IOC to ICM, and from baseline to ICM and 1.19 (1.17-1.21), 1.18 (1.15-1.21), and 1.12 (1.05-1.19) for mortality risk from baseline to all-cause death, from IOC to all-cause death, and from ICM to all-cause death, respectively.

Conclusions

Our study revealed that LAFs have a stronger impact on morbidity outcomes than on morbidity outcomes. These findings provide evidence to develop strategies for managing the trajectory of ICM.

Associations between socioeconomic inequalities and progression to psychological and cognitive multimorbidities after onset of a physical condition: a multicohort study

Background

Chronic physical conditions (e.g., heart diseases, diabetes) increase with population ageing, contributing to psychological and cognitive multimorbidities. Yet, little is known about socioeconomic inequalities in this process. We examined the associations between socioeconomic status (SES) and progression to psychological and cognitive multimorbidities after onset of a physical condition.

Methods

We used harmonized individual-level data from five prospective cohort studies across 24 countries in the US, Europe and Asia, with repeated morbidity measurements between 2002 and 2021. Participants with at least one new-onset physical conditions (hypertension, diabetes, heart diseases, stroke, chronic lung diseases, cancer, or arthritis) were followed up for progression to physical-psychological multimorbidity, physical-cognitive multimorbidity, and physical-psychological-cognitive multimorbidity. SES was determined based on educational level and total household wealth at the onset of a physical condition. Time to and incidence rates of progressing psychological and cognitive multimorbidities were estimated in analyses stratified by SES. Fine-Gray subdistribution hazard models and multi-state models were used to estimate the associations between SES and progression to psychological and cognitive multimorbidities.

Findings

Among 20,250 participants aged ≥45 years (mean age at a physical condition onset 65.38 years, standard deviation 8.37) with at least one new-onset physical conditions in the analysis, 7928 (39.2%) progressed to psychological and cognitive multimorbidities during a median follow-up of 8.0 years (168,575 person-years). The mean survival time free from physical-psychological-cognitive multimorbidity was 11.96 years (95% confidence interval 11.57-12.34) in low SES individuals, compared to 15.52 years (15.40-15.63) in high SES individuals, with the corresponding incidence rate of 18.44 (16.32-20.82) and 3.15 (2.48-4.01) per 1000 person-years, respectively. The associations of education, household wealth and SES with multimorbidities followed a dose-dependent relation, with subdistribution hazard ratios per decreasing SES category being 1.24 (1.19-1.29) for physical-psychological multimorbidity, 1.47 (1.40-1.54) for physical-cognitive multimorbidity, and 1.84 (1.72-1.97) for physical-psychological-cognitive multimorbidity. The strongest SES-multimorbidities associations were observed in participants with arthritis, hypertension or diabetes. In multi-state models SES was linked to all five transitions from physical condition to physical-psychological multimorbidity, physical-cognitive multimorbidity and physical-psychological-cognitive multimorbidity.

Interpretation

Socioeconomic inequalities are associated with the progression of a chronic physical condition, with the lower SES groups had both an earlier time to and a higher incidence of psychological and cognitive multimorbidities. These findings underscore the need for more effective equity-oriented policies and healthcare practices to address reduced psychological wellness and cognitive maintenance among individuals with low SES and physical conditions.

Funding

Zhejiang University Hundred Talents Program Research Initiation Fund, Fundamental Research Funds for the Central Universities in China, Wellcome Trust, Medical Research Council, National Institute on Aging, Academy of Finland.

Reverse causation bias: A simulation study comparing first- and second-line treatments with an overlap of symptoms between treatment indication and studied outcome

BACKGROUND

Reverse causation is a challenge in many drug-cancer associations, where the cancer symptoms are potentially mistaken for drug indication symptoms. However, tools to assess the magnitude of this type of bias are currently lacking. We used a simulation-based approach to investigate the impact of reverse causation on the association between the use of topical tacrolimus and cutaneous T-cell lymphoma (CTCL) in a multinational, population-based study using topical corticosteroids (TCS) as comparator.

METHODS

We used a multistate model to simulate patients' use over time of a first- (TCS) and second-line treatment (topical tacrolimus), onset of atopic dermatitis (indication for drugs) and CTCL (the studied outcome). We simulated different scenarios to mimic real-life use of the two treatments. In all scenarios, it was assumed that there was no causal effect of the first- or second-line treatment on the occurrence of CTCL. Simulated data were analysed using Cox proportional hazards models.

RESULTS

The simulated hazard ratios (HRs) of CTCL for patients treated with tacrolimus vs. TCS were consistently above 1 in all 9 settings in the main scenario. In our main analysis, we observed a median HR of 3.09 with 95% of the observed values between 2.11 and 4.69.

CONCLUSIONS

We found substantial reverse causation bias in the simulated CTCL risk estimates for patients treated with tacrolimus vs. TCS. Reverse causation bias may result in a false positive association between the second-line treatment and the studied outcome, and this simulation-based framework can be adapted to quantify the potential reverse causation bias.

Calibration plots for multistate risk predictions models

INTRODUCTION

There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation.

METHODS

We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records.

RESULTS

The pseudo-value, BLR-IPCW, and MLR-IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low-density regions of predicted transition probability.

CONCLUSIONS

We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN.

Multi-state Markov model for time to treatment changes for HIV/AIDS patients: a retrospective cohort national datasets, Ethiopia

BACKGROUND

Virological failure, drug resistance, toxicities, and other issues make it difficult for ART to maintain long-term sustainability. These issues would force a modification in the patient's treatment plan. The aim of this research was to determine whether first-line antiretroviral therapy is durable and to identify the factors that lead to patients on HAART changing their first highly active antiretroviral therapy regimen.

METHODS

A retrospective cohort study was conducted from October, 2019-March, 2020 across all regional states including Addis Ababa and Dire Dawa administrative cities. The target population is from all health facilities that have been providing ART service for at least the past 6 months as of October 2019. Multi-stage clustered sampling method was used to select study facilities and participants. Simple random selected ART medical records of patients ever enrolled in ART treatment services. We adopted a multi-state survival modelling (msm) approach assuming each treatment regimen as state. We estimate the transition probability of patients to move from one regimen to another for time to treatment change/switch. We estimated the transition probability, prediction probabilities and length of stay and factor associated with treatment modification of patients to move from one regimen to another.

RESULTS

Any of the six therapy combinations (14.4%) altered their treatment at least once during the follow-up period for a variety of reasons. Of the patients, 4,834 (13.26%) changed their treatments just once, while 371 (1.1%) changed it more than once. For 38.6% of the time, a treatment change was undertaken due to toxicity, another infection or comorbidity, or another factor, followed by New drugs were then made accessible and other factors 18.3% of the time, a drug was out of supply; 2.6% of those instances involved pregnancy; and 43.1% involved something else. Highly active anti-retroviral therapy (HAART) combinations TDF + 3TC + NVP, d4T + 3TC + NVP, and TDF + 3TC + EFV were high to treatment alterations in all reasons of treatment modifications, with 29.74%, 26.52%, and 19.52% treatment changes, respectively. Early treatment modification or regime change is one of the treatment combinations that include the d4T medication that creates major concern. The likelihood of staying and moving at the the start of s = 0 and 30-month transitions increased, but the likelihood of staying were declined. For this cohort dataset, the presence of opportunistic disease, low body weight, baseline CD4 count, and baseline TB positive were risk factors for therapy adjustment.

CONCLUSION

Given that the current study took into account a national dataset, it provides a solid basis for ART drug status and management. The patient had a higher likelihood of adjusting their treatment at some point during the follow-up period due to drug toxicity, comorbidity, drug not being available, and other factors, according to the prediction probability once more. Baseline TB positivity, low CD4 count, opportunistic disease, and low body weight were risk factors for therapy adjustment in this cohort dataset.

Benefits and risks of clofarabine in adult acute lymphoblastic leukemia investigated in depth by multi-state modeling

BACKGROUND

We recently reported results of the prospective, open-label HOVON-100 trial in 334 adult patients with acute lymphoblastic leukemia (ALL) randomized to first-line treatment with or without clofarabine (CLO). No improvement of event-free survival (EFS) was observed, while a higher proportion of patients receiving CLO obtained minimal residual disease (MRD) negativity.

AIM

In order to investigate the effects of CLO in more depth, two multi-state models were developed to identify why CLO did not show a long-term survival benefit despite more MRD-negativity.

METHODS

The first model evaluated the effect of CLO on going off-protocol (not due to refractory disease/relapse, completion or death) as a proxy of severe treatment-related toxicity, while the second model evaluated the effect of CLO on obtaining MRD negativity. The subsequent impact of these intermediate events on death or relapsed/refractory disease was assessed in both models.

RESULTS

Overall, patients receiving CLO went off-protocol more frequently than control patients (35/168 [21%] vs. 18/166 [11%], p = 0.019; HR 2.00 [1.13-3.52], p = 0.02), especially during maintenance (13/44 [30%] vs. 6/56 [11%]; HR 2.85 [95%CI 1.08-7.50], p = 0.035). Going off-protocol was, however, not associated with more relapse or death. Patients in the CLO arm showed a trend towards an increased rate of MRD-negativity compared with control patients (HR MRD-negativity: 1.35 [0.95-1.91], p = 0.10), which did not translate into a significant survival benefit.

CONCLUSION

We conclude that the intermediate states, i.e., going off-protocol and MRD-negativity, were affected by adding CLO, but these transitions were not associated with subsequent survival estimates, suggesting relatively modest antileukemic activity in ALL.

Association of body mass index with risk of cardiometabolic disease, multimorbidity and mortality: a multi-state analysis based on the Kailuan cohort

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.

Roles of general and central adiposity in cardiometabolic multimorbidity: revisiting the obesity paradox using a multistate model

OBJECTIVE

The objective of this study was to evaluate the associations of general and central obesity with risk of first cardiometabolic disease (FCMD), cardiometabolic multimorbidity (CMM), and death.

METHODS

A total of 86,169 participants who were CMD-free were included from the Kailuan cohort and categorized into four groups by quartiles of BMI, waist to hip ratio (WHR), weight-adjusted waist index, and waist to height ratio. We defined FCMD as the first onset of diabetes, stroke, or myocardial infarction and CMM as co-occurrence of at least two CMDs. Multistate models were used to estimate hazard ratios and 95% CI.

RESULTS

A total of 18,461 participants developed FCMD, of whom 1476 progressed to CMM, and 10,009 died during follow-ups. Both general and central adiposity indices increased the risk of transition from baseline to FCMD and from FCMD to CMM. However, compared with the first quartile, the hazard ratio (95% CI) of the fourth quartile of BMI was 0.86 (95% CI: 0.80-0.91) for transition from health to death and 0.66 (95% CI: 0.59-0.74) from FCMD to death, whereas the corresponding estimates of WHR were 1.22 (95% CI: 1.14-1.31) and 1.16 (95% CI: 1.02-1.32), respectively.

CONCLUSIONS

Central adiposity indices such as WHR were associated with an increased risk of CMD and mortality, showing no evidence for the obesity paradox and thereby supporting a shift of public focus from BMI only to both general obesity and adiposity distribution.

Dynamic nature of BRAF or KRAS p.G12C mutations in second-line therapy for advanced colorectal cancer patients: do early and late effects exist?

INTRODUCTION

The mitogen-activated protein kinase (MAPK) signalling network aberrations in metastatic colorectal cancer (mCRC) generate intrinsic dynamic effects and temporal variations that are crucial but often overlooked in clinical trial populations. Here, we investigate the time-varying impact of MAPK pathway mutation genotype on each treatment line's contribution to the overall clinical course.

METHODS

The PROMETEO study focused on mCRC patients undergoing second-line treatment at 20 hospitals. We evaluated genotypes and employed flexible models to analyse the dynamic effect of each mutation.

RESULTS

We examined data derived from 1160 patients. The effects of KRAS G12C or G12V, and BRAF V600E are clearly time-varying, with unexpected consequences such as the deleterious effect of BRAF V600E vs other genotypes dissipating over time when subjects receive antiangiogenics, or KRAS G12V and G12C showing increasing aggressiveness over time. Thus, contrary to expectations, the 12-month survival rate from the second line for those who survived >6 months was 49.9% (95% CI, 32.7-67.3) for KRAS G12C and 59% (95% CI, 38.5-80.6) for BRAF V600E.

CONCLUSIONS

The dynamic perspective is essential for understanding the behaviour of tumours with specific genotypes, especially from the second line onward. This may be relevant in patient monitoring and treatment decision-making, particularly in cases with distinct mutations.

Accelerated biological aging elevates the risk of cardiometabolic multimorbidity and mortality

Associations of biological aging with the development and mortality of cardiometabolic multimorbidity (CMM) remain unclear. Here we conducted a multistate analysis in 341,159 adults of the UK Biobank. CMM was defined as the coexistence of two or three cardiometabolic diseases (CMDs), including type 2 diabetes, ischemic heart disease and stroke. Biological aging was measured using the Klemera-Doubal Method Biological Age and PhenoAge algorithms. Over a median follow-up of 8.84 years, biologically older participants demonstrated robust higher risks from first CMD to CMM and then to death. In particular, adjusted hazard ratios for first CMD to CMM and for CMM to death were 1.15 (95% confidence interval (CI): 1.12, 1.19) and 1.26 (95% CI: 1.17, 1.35) per 1 s.d. increase in PhenoAge acceleration, respectively. Compared with frailty, Framingham Risk Score and Systematic Coronary Risk Evaluation 2 (SCORE2), biological aging measures yielded consistent substantial associations with CMM development. Accelerated biological aging may help identify individuals with CMM risks, potentially enabling early intervention and subclinical prevention.

Merkel cell carcinoma recurrence risk estimation is improved by integrating factors beyond cancer stage: A multivariable model and web-based calculator

BACKGROUND

Merkel cell carcinoma (MCC) recurs in 40% of patients. In addition to stage, factors known to affect recurrence risk include: sex, immunosuppression, unknown primary status, age, site of primary tumor, and time since diagnosis.

PURPOSE

Create a multivariable model and web-based calculator to predict MCC recurrence risk more accurately than stage alone.

METHODS

Data from 618 patients in a prospective cohort were used in a competing risk regression model to estimate recurrence risk using stage and other factors.

RESULTS

In this multivariable model, the most impactful recurrence risk factors were: American Joint Committee on Cancer stage (P < .001), immunosuppression (hazard ratio 2.05; P < .001), male sex (1.59; P = .003) and unknown primary (0.65; P = .064). Compared to stage alone, the model improved prognostic accuracy (concordance index for 2-year risk, 0.66 vs 0.70; P < .001), and modified estimated recurrence risk by up to 4-fold (18% for low-risk stage IIIA vs 78% for high-risk IIIA over 5 years).

LIMITATIONS

Lack of an external data set for model validation.

CONCLUSION/RELEVANCE

As demonstrated by this multivariable model, accurate recurrence risk prediction requires integration of factors beyond stage. An online calculator based on this model (at merkelcell.org/recur) integrates time since diagnosis and provides new data for optimizing surveillance for MCC patients.

Increased susceptibility to new-onset atrial fibrillation in diabetic women with poor sleep behaviour traits: findings from the prospective cohort study in the UK Biobank

BACKGROUND

Diabetic individuals often encounter various sleep-related challenges. Although the association between sleep duration and atrial fibrillation (AF) have been explored, the association of other sleep traits with the incidence of AF remains unclear. A comprehensive understanding of these traits is essential for a more accurate assessment of sleep conditions in patients with diabetes and the development of novel AF prevention strategies.

METHODS

This study involved 23,785 patients with diabetes without any pre-existing cardiovascular disease, drawn from the UK Biobank. Sleep behaviour traits examined encompassed sleep duration, chronotype, insomnia, snoring and daytime sleepiness. Sleep duration was categorised into three groups: low (≤ 5 h), proper (6-8 h) and long (≥ 9 h). We assessed associations using multivariate Cox proportional risk regression models. Furthermore, four poor sleep behaviours were constructed to evaluate their impact on the risk of new-onset AF.

RESULTS

Over a mean follow-up period of 166 months, 2221 (9.3%) new cases of AF were identified. Short (hazard ratio (HR), 1.28; 95% confidence interval (CI) 1.10-1.50) and long sleep durations (HR 1.16; 95% CI 1.03-1.32) consistently exhibited an elevated risk of AF compared to optimal sleep duration. Early chronotype, infrequent insomnia and daytime sleepiness were associated with 11% (HR 0.89; 95% CI 0.82-0.97), 15% (HR 0.85; 95% CI 0.77-0.95) and 12% (HR 0.88; 95% CI 0.81-0.96) reduced risk of new-onset AF, respectively. However, no significant association was found between snoring and the incidence of AF (HR 0.99; 95% CI 0.91-1.07).

CONCLUSIONS

In diabetic populations, sleep duration, chronotype, insomnia and daytime sleepiness are strongly associated with AF incidence. An optimal sleep duration of 6-8 h presents the lowest AF risk compared to short or long sleep duration. Additionally, poor sleep patterns present a greater risk of new-onset AF in women than in men.

Allogeneic hematopoietic stem cell transplantation in patients aged 60-79 years in Germany (1998-2018): a registry study

Incidences of diseases treated with transplantation frequently peak at higher age. The contribution of age to total risk of transplantation has not been estimated amidst an aging society. We compare outcomes of 1,547 patients aged 70-79 years and 9,422 patients aged 60-69 years transplanted 1998-2018 for myeloid, lymphoid and further neoplasia in Germany. To quantify the contribution of population mortality to survival, we derive excess mortality based on a sex-, year- and agematched German population in a multistate model that incorporates relapse and graft-versus-host-disease (GvHD). Overall survival, relapse-free survival (RFS) and GvHD-free-relapse-free survival (GRFS) is inferior in patients aged 70-79 years, compared to patients aged 60-69 years, with 36% (95% Confidence Interval [CI]: 34-39%) versus 43% (41-44%), 32% (30- 35%) versus 36% (35-37%) and 23% (21-26%) versus 27% (26-28%) three years post-transplant (P<0.001). Cumulative incidences of relapse at three years are 27% (25-30%) for patients aged 70-79 versus 29% (29-30%) (60-69 years) (P=0.71), yet the difference in non-relapse mortality (NRM) (40% [38-43%] vs. 35% [34-36%] in patients aged 70-79 vs. 60-69 years) (P<0.001) translates into survival differences. Median OS of patients surviving >1 year relapse-free is 6.7 (median, 95% CI: 4.5-9.4, 70-79 years) versus 9 (8.4-10.1, 60-69 years) years since landmark. Three years after RFS of one year, excess NRM is 14% (95% CI: 12-18%) in patients aged 70-79 versus 12% [11-13%] in patients aged 60-69, while population NRM is 7% (6-7%) versus 3% (3-3%). Mortality for reasons other than relapse, GvHD, or age is as high as 27% (24-29%) and 22% (22-23%) four years after transplantation. In conclusion, survival amongst older patients is adequate after allogeneic stem cell transplantation.

MS-CPFI: A model-agnostic Counterfactual Perturbation Feature Importance algorithm for interpreting black-box Multi-State models

Multi-state processes (Webster, 2019) are commonly used to model the complex clinical evolution of diseases where patients progress through different states. In recent years, machine learning and deep learning algorithms have been proposed to improve the accuracy of these models' predictions (Wang et al., 2019). However, acceptability by patients and clinicians, as well as for regulatory compliance, require interpretability of these algorithms's predictions. Existing methods, such as the Permutation Feature Importance algorithm, have been adapted for interpreting predictions in black-box models for 2-state processes (corresponding to survival analysis). For generalizing these methods to multi-state models, we introduce a novel model-agnostic interpretability algorithm called Multi-State Counterfactual Perturbation Feature Importance (MS-CPFI) that computes feature importance scores for each transition of a general multi-state model, including survival, competing-risks, and illness-death models. MS-CPFI uses a new counterfactual perturbation method that allows interpreting feature effects while capturing the non-linear effects and potentially capturing time-dependent effects. Experimental results on simulations show that MS-CPFI increases model interpretability in the case of non-linear effects. Additionally, results on a real-world dataset for patients with breast cancer confirm that MS-CPFI can detect clinically important features and provide information on the disease progression by displaying features that are protective factors versus features that are risk factors for each stage of the disease. Overall, MS-CPFI is a promising model-agnostic interpretability algorithm for multi-state models, which can improve the interpretability of machine learning and deep learning algorithms in healthcare.

Increased risk of aseptic loosening for posterior stabilized compared with posterior cruciate-retaining uncemented total knee replacements: a cohort study of 13,667 knees from the Dutch Arthroplasty Registry

BACKGROUND AND PURPOSE

While registry studies have suggested a higher risk of revision for posterior-stabilized (PS) compared with posterior cruciate-retaining (CR) total knee replacements (TKR) using cement, it is unknown whether this is also the case for uncemented TKR. We aimed to compare the revision rates of PS and CR designs in patients receiving primary uncemented TKR.

PATIENTS AND METHODS

Data from the Dutch arthroplasty register (LROI) was analyzed, comprising 12,226 uncemented primary CR TKRs and 750 uncemented PS TKRs registered between 2007 and 2022. Competing risk and multivariable Cox regression analyses were used to compare revision rates, risks of revision, and reasons for revision between groups. Sensitivity analyses were performed to analyze the risk, concerning the 5 most commonly used implants and performing hospitals for each group.

RESULTS

Uncemented PS TKRs had higher 10-year revision rates for any reason and aseptic loosening (6.5%, 95% confidence interval [CI] 4.6-9.2 and 3.9%, CI 2.6-6.7) compared with uncemented CR TKRs (4.2%, CI 3.8-4.7 and 1.4%, CI 1.2-1.7). PS TKRs were 1.4 and 2.5 times more likely to be revised for any reason and aseptic loosening, respectively. These results remained consistent after adjustment for age, sex, BMI, previous surgeries, bearing mobility, and surface modification, with sensitivity analyses.

CONCLUSION

We found that uncemented PS implants have a higher rate of revision than uncemented CR implants, mainly due to a higher risk of aseptic loosening.

Distinct on-treatment HCC risks associated with different decompensation events in HBV patients with cirrhosis

OBJECTIVES

Long-term treatment with nucleoside analog (NA) reduces the risks for decompensation and hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) patients with compensated cirrhosis (CC). However, whether antiviral therapy has differential efficacy on the risks for decompensation and HCC is insufficiently elucidated. Therefore, we investigated the disease state transition, focusing on decompensation event-specific HCC risk in NA-treated CHB patients with CC.

METHODS

We prospectively followed up on 1163 NA-treated CHB patients with CC every six months for up to seven years. The cumulative incidence and risk of HCC were analyzed by the Kaplan-Meier method and competing risk model. The multistate model was used to estimate the transition probabilities to HCC from different disease states.

RESULTS

HCC predominated the first liver-related events, with a 5-year cumulative incidence of 9.0%, followed by decompensation (8.3%, including 7.9% nonbleeding decompensation and 2.4% variceal bleeding) and 0.2% death. The decompensation stage had a significantly higher 5-year cumulative HCC incidence than the CC stage (27.6% vs. 9.1%; HR = 2.42, 95% CI: 1.24, 4.71). Furthermore, nonbleeding decompensation events had a higher 5-year transition probability to HCC than bleeding (27.6% vs. 15.8%; HR = 2.69, 95% CI: 1.41, 4.17). Viral suppression modified the on-treatment transition risk to HCC (1-year: HR = 0.45, 95% CI: 0.28, 0.73; 3-year: HR = 0.23, 95% CI: 0.14, 0.38). An online calculator was developed to facilitate HCC risk stratification.

CONCLUSIONS

In NA-treated CHB patients with compensated cirrhosis, the risk was higher for HCC than for decompensation; more importantly, different decompensation events conferred distinct HCC risks.

Long-term influence of air pollutants on morbidity and all-cause mortality of cardiometabolic multi-morbidity: A cohort analysis of the UK Biobank participants

BACKGROUND

The effects of air pollutants on cardiometabolic diseases (CMDs) have been widely explored, whereas their influences on cardiometabolic multi-morbidity (CMM) were not clear.

METHODS

We employed the UK Biobank cohort (N = 317,160) to study the association between six air pollutants (PM2.5, PM10, PM2.5-10, PM2.5abs, NO2, and NOx) and four CMDs including type II diabetes (T2D), coronary artery disease (CAD), stroke and hypertension. CMM was defined as occurrence of two or more of the four diseases. Multi-state Cox models were performed to estimate hazard ratio (HR) and its 95% confidence interval (95%CI).

RESULTS

During a median follow-up of 12.8 years, 52,211 participants developed only one CMD, 15,446 further developed CMM, and 16,861 ultimately died. It was demonstrated that per interquartile range increase (IQR) increases in PM2.5, PM10, PM2.5-10, PM2.5abs, NO2, and NOx would increase 12% (9%-15%), 4% (1%-7%), 3% (1%-6%), 7% (4%-10%), 11% (8%-15%) and 10% (7%-13%) higher risk of developing one CMD from health baseline; 7% (2%-12%), 8% (3%-13%), 6% (2%-11%), 10% (5%-15%), 13% (7%-18%) and 10% (5%-15%) greater risk of occurring CMM from one CMD baseline; and 11% (-2%∼26%), 22% (7%-38%), 17% (3%-32%), 31% (16%-49%), 33% (17%-51%) and 32% (17%-50%) larger risk of causing death from CMM baseline, respectively.

CONCLUSIONS

We revealed that people living in areas with high air pollution suffered from higher hazard of CMD, CMM and all-cause mortality; our findings implied keeping clean air was an effective approach to prevent or mitigate initiation, progression, and death from healthy to CMDs and from CMDs to CMM.

Preserved Ratio Impaired Spirometry and Risks of Macrovascular, Microvascular Complications and Mortality Among Individuals With Type 2 Diabetes

BACKGROUND

The prospective associations of preserved ratio impaired spirometry (PRISm) with new-onset macrovascular and microvascular complications and mortality among individuals with type 2 diabetes (T2D) and whether PRISm enhances the prediction ability of an established office-based risk score remain to be elucidated.

RESEARCH QUESTION

Can PRISm be used as a predictor of poor prognosis in individuals with T2D?

STUDY DESIGN AND METHODS

We included 20,047 study participants with T2D and complete data on spirometry at recruitment from the UK Biobank cohort. Multivariable Cox proportional hazards models were used to assess the associations of baseline PRISm (FEV1 to FVC ratio, ≥ 0.70; FEV1, < 80% predicted) with subsequent risks of incident stroke (any type), ischemic stroke, myocardial infarction, unstable angina, coronary heart disease, diabetic retinopathy, diabetic kidney disease, all-cause mortality, cardiovascular mortality, and respiratory mortality.

RESULTS

For this cohort analysis, 4,521 patients (22.55% of participants with T2D) showed comorbid PRISm at baseline. Over a median follow-up of 11.52 to 11.87 years, patients with T2D with PRISm at baseline showed higher risks than those with normal spirometry findings of various T2D complications developing and mortality; the adjusted hazard ratios for PRISm were 1.413 (95% CI, 1.187-1.681) for stroke (any type), 1.382 (95% CI, 1.129-1.690) for ischemic stroke, 1.253 (95% CI, 1.045-1.503) for myocardial infarction, 1.206 (95% CI, 1.086-1.339) for coronary heart disease, 1.311 (95% CI, 1.141-1.506) for diabetic retinopathy, 1.384 (95% CI, 1.190-1.610) for diabetic kidney disease, 1.337 (95% CI, 1.213-1.474) for all-cause mortality, 1.597 (95% CI, 1.296-1.967) for cardiovascular mortality, and 1.559 (95% CI, 1.189-2.044) for respiratory mortality, respectively. The addition of PRISm significantly improved the reclassification ability, based on the net reclassification index, of an office-based risk score by 15.53% (95% CI, 10.14%-19.63%) to 33.60% (95% CI, 20.90%-45.79%).

INTERPRETATION

Individuals with T2D with comorbid PRISm, accounting for a considerable proportion of the population with T2D, showed significantly increased risks of adverse macrovascular and microvascular complications and mortality.

Validation of a Model for Targeted EEG Monitoring Duration in Critically Ill Children

PURPOSE

Continuous EEG monitoring (CEEG) to identify electrographic seizures (ES) in critically ill children is resource intense. Targeted strategies could enhance implementation feasibility. We aimed to validate previously published findings regarding the optimal CEEG duration to identify ES in critically ill children.

METHODS

This was a prospective observational study of 1,399 consecutive critically ill children with encephalopathy. We validated the findings of a multistate survival model generated in a published cohort ( N = 719) in a new validation cohort ( N = 680). The model aimed to determine the CEEG duration at which there was <15%, <10%, <5%, or <2% risk of experiencing ES if CEEG were continued longer. The model included baseline clinical risk factors and emergent EEG risk factors.

RESULTS

A model aiming to determine the CEEG duration at which a patient had <10% risk of ES if CEEG were continued longer showed similar performance in the generation and validation cohorts. Patients without emergent EEG risk factors would undergo 7 hours of CEEG in both cohorts, whereas patients with emergent EEG risk factors would undergo 44 and 36 hours of CEEG in the generation and validation cohorts, respectively. The <10% risk of ES model would yield a 28% or 64% reduction in CEEG hours compared with guidelines recommending CEEG for 24 or 48 hours, respectively.

CONCLUSIONS

This model enables implementation of a data-driven strategy that targets CEEG duration based on readily available clinical and EEG variables. This approach could identify most critically ill children experiencing ES while optimizing CEEG use.

Clinical courses of acute kidney injury in hospitalized patients: a multistate analysis

Persistence of acute kidney injury (AKI) or insufficient recovery of renal function was associated with reduced long-term survival and life quality. We quantified AKI trajectories and describe transitions through progression and recovery among hospitalized patients. 245,663 encounters from 128,271 patients admitted to UF Health between 2012 and 2019 were retrospectively categorized according to the worst AKI stage experienced within 24-h periods. Multistate models were fit for describing characteristics influencing transitions towards progressed or regressed AKI, discharge, and death. Effects of age, sex, race, admission comorbidities, and prolonged intensive care unit stay (ICU) on transition rates were examined via Cox proportional hazards models. About 20% of encounters had AKI; where 66% of those with AKI had Stage 1 as their worst AKI severity during hospitalization, 18% had Stage 2, and 16% had Stage 3 AKI (12% with kidney replacement therapy (KRT) and 4% without KRT). At 3 days following Stage 1 AKI, 71.1% (70.5-71.6%) were either resolved to No AKI or discharged, while recovery proportion was 38% (37.4-38.6%) and discharge proportion was 7.1% (6.9-7.3%) following AKI Stage 2. At 14 days following Stage 1 AKI, patients with additional frail conditions stay had lower transition proportion towards No AKI or discharge states. Multistate modeling framework is a facilitating mechanism for understanding AKI clinical course and examining characteristics influencing disease process and transition rates.

Associations of Sleep Patterns With Dynamic Trajectory of Cardiovascular Multimorbidity and Mortality: A Multistate Analysis of a Large Cohort

Background The purpose of this study was to explore the association of sleep patterns with the development of first cardiovascular diseases (FCVD), progression to cardiovascular multimorbidity (CVM), and subsequently to mortality. Methods and Results This prospective study included 381 179 participants without coronary heart disease, stroke, atrial fibrillation, or heart failure at baseline, and they were followed up until March 31, 2021. We generated sleep patterns by summing the scores for 5 sleep behaviors, whereby <7 or >8 hours/d of sleep, evening chronotype, frequent insomnia, snoring, and daytime dozing were defined as high-risk groups. We used a multistate model to estimate the impacts of sleep patterns on the dynamic progression of cardiovascular diseases. Over a median follow-up of 12.1 years, 41 910 participants developed FCVD, 7302 further developed CVM, and 20 707 died. We found that adverse sleep patterns were significantly associated with the transition from health to FCVD, from FCVD to CVM, and from health to death, with hazard ratio associated with 1-factor increase in sleep scores being 1.08 (95% CI, 1.07-1.09), 1.04 (95% CI, 1.02-1.06), and 1.04 (95% CI, 1.02-1.05), respectively. When further dividing FCVD into coronary heart disease, stroke, atrial fibrillation, and heart failure, adverse sleep patterns showed a significant and persistent effect on the transition from health to each cardiovascular disease, and from heart failure or atrial fibrillation to CVM. Conclusions Our study provides evidence that adverse sleep patterns might increase the risk for the progression from health to cardiovascular diseases and further to CVM. Our findings suggest that improving sleep behaviors might be helpful for the primary and secondary prevention of cardiovascular diseases.

Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques

INTRODUCTION

This study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis.

METHODS

We considered five methods: product (multiply marginal risks), dual-outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models. We assessed calibration and discrimination under a variety of simulated data scenarios, varying outcome prevalence, and the amount of residual correlation. The simulation focused on model misspecification and statistical power. Using data from the Clinical Practice Research Datalink, we compared model performance when predicting the risk of cardiovascular disease and type 2 diabetes both occurring.

RESULTS

Discrimination was similar for all methods. The product method was poorly calibrated in the presence of residual correlation. The msm and dual-outcome models were the most robust to model misspecification but suffered a drop in performance at small sample sizes due to overfitting, which the copula and frailty model were less susceptible to. The copula and frailty model's performance were highly dependent on the underlying data structure. In the clinical example, the product method was poorly calibrated when adjusting for 8 major cardiovascular risk factors.

DISCUSSION

We recommend the dual-outcome method for predicting the risk of two survival outcomes both occurring. It was the most robust to model misspecification, although was also the most prone to overfitting. The clinical example motivates the use of the methods considered in this study.

Association of ultra-processed food consumption with incident depression and anxiety: a population-based cohort study

Background: Global ultra-processed food (UPF) consumption has risen rapidly. The development and prognosis of depression and anxiety remain unclarified. Herein, we aimed to examine the association between UPF consumption and the incidence and progression trajectory of depression and anxiety. Methods: In our study, participants were recruited between 2006 and 2010. UPF consumption was expressed as UPF servings, energy ratio, and weight ratio. The relationships between UPF consumption and depression or anxiety were assessed using the Cox proportional hazards model. Multi-state models were used to explore the association between UPF consumption and the risks of all transitions from a healthy state to depression or anxiety and then to all-cause mortality. Results: Among the 183 474 participants, 5453 were diagnosed with depression and 6763 with anxiety during the follow-up of 13.1 years. The participants in the highest quartile (Q4) of UPF servings, energy ratio, and weight ratio had an increased risk of depression compared to those in the lowest quartile (Q1), with hazard ratios (HRs) and 95% confidence intervals [CIs] of 1.22 (1.13-1.31), 1.13 (1.05-1.22), and 1.26 (1.17-1.36), respectively. Similarly, participants in Q4 of UPF consumption had a higher risk of anxiety, with HRs (95% CIs) of 1.13 (1.06-1.21), 1.13 (1.05-1.21), and 1.11 (1.04-1.19), compared to those in Q1. The study also found a significant association between UPF consumption and all-cause mortality, which disappeared for participants with depression or anxiety. Conclusions: Our findings revealed that UPF consumption is associated with depression or anxiety.

The association between disability progression, relapses, and treatment in early relapse onset MS: an observational, multi-centre, longitudinal cohort study

The indirect contribution of multiple sclerosis (MS) relapses to disability worsening outcomes, and vice-versa, remains unclear. Disease modifying therapies (DMTs) are potential modulators of this association. Understanding how these endo-phenotypes interact may provide insights into disease pathogenesis and treatment practice in relapse-onset MS (ROMS). Utilising a unique, prospectively collected clinical data from a longitudinal cohort of 279 first demyelinating event cases followed for up to 15 years post-onset, we examined indirect associations between relapses and treatment and the risk of disability worsening, and vice-versa. Indirect association parameters were estimated using joint models for longitudinal and survival data. Early relapses within 2.5 years of MS onset predicted early disability worsening outcomes (HR = 3.45, C.I 2.29-3.61) per relapse, but did not contribute to long-term disability worsening thereinafter (HR = 0.21, C.I 0.15-0.28). Conversely, disability worsening outcomes significantly contributed to relapse risk each year (HR = 2.96, C.I 2.91-3.02), and persisted over time (HR = 3.34, C.I 2.90-3.86), regardless of DMT treatments. The duration of DMTs significantly reduced the hazards of relapses (1st-line DMTs: HR = 0.68, C.I 0.58-0.79; 3rd-line DMTs: HR = 0.37, C.I 0.32-0.44) and disability worsening events (1st-line DMTs: HR = 0.74, C.I 0.69-0.79; 3rd-line DMTs: HR = 0.90, C.I 0.85-0.95), respectively. Results from time-dynamic survival probabilities further revealed individuals having higher risk of future relapses and disability worsening outcomes, respectively. The study provided evidence that in ROMS, relapses accrued within 2.5 years of MS onset are strong indicators of disability worsening outcomes, but late relapses accrued 2.5 years post onset are not overt risk factors for further disability worsening. In contrast, disability worsening outcomes are strong positive predictors of current and subsequent relapse risk. Long-term DMT use and older age strongly influence the individual outcomes and their associations.

Comparing Individualized Survival Predictions From Random Survival Forests and Multistate Models in the Presence of Missing Data: A Case Study of Patients With Oropharyngeal Cancer

Background

In recent years, interest in prognostic calculators for predicting patient health outcomes has grown with the popularity of personalized medicine. These calculators, which can inform treatment decisions, employ many different methods, each of which has advantages and disadvantages.

Methods

We present a comparison of a multistate model (MSM) and a random survival forest (RSF) through a case study of prognostic predictions for patients with oropharyngeal squamous cell carcinoma. The MSM is highly structured and takes into account some aspects of the clinical context and knowledge about oropharyngeal cancer, while the RSF can be thought of as a black-box non-parametric approach. Key in this comparison are the high rate of missing values within these data and the different approaches used by the MSM and RSF to handle missingness.

Results

We compare the accuracy (discrimination and calibration) of survival probabilities predicted by both approaches and use simulation studies to better understand how predictive accuracy is influenced by the approach to (1) handling missing data and (2) modeling structural/disease progression information present in the data. We conclude that both approaches have similar predictive accuracy, with a slight advantage going to the MSM.

Conclusions

Although the MSM shows slightly better predictive ability than the RSF, consideration of other differences are key when selecting the best approach for addressing a specific research question. These key differences include the methods' ability to incorporate domain knowledge, and their ability to handle missing data as well as their interpretability, and ease of implementation. Ultimately, selecting the statistical method that has the most potential to aid in clinical decisions requires thoughtful consideration of the specific goals.

Sickness absence transitions among Swedish twins with back, neck or shoulder pain and common mental disorders applying a multi-state approach

We aimed to investigate transitions to and from sickness absence, or disability pension among individuals with back, neck, or shoulder pain and/or with common mental disorders (CMDs), and the role of familial (genetics and shared environment) influences on the transitions. Swedish twins born 1935-1985 who responded to pain and CMDs survey items (N = 41,516) were followed on average 8.7 years for sickness absence states in national registers. Multi-state Cox regression models were applied for three exposure groups: pain, CMDs, and presence of both, compared to unexposed. Exposure discordant twin pairs, stratified by zygosity, were analysed to assess the role of familial factors. Hazard Ratios (HR) with 95% confidence intervals and transition intensities were calculated. HRs were similar for transitions between states among those with pain or CMDs. The highest HRs were for transitions from entry to sickness absence and sickness absence to disability pension among those with both pain and CMDs (HRs: 1.61 and 1.43, respectively). Higher HRs for dizygotic compared to monozygotic twins for the first transition to sickness absence and for altering back to not being sickness absent indicate familial confounding. Back, neck, or shoulder pain and/or CMDs indicate a higher risk to become sickness absent and for repeated sickness absence episodes over time compared to unaffected.

Cardiovascular Complications Are the Primary Drivers of Mortality in Hospitalized Patients With SARS-CoV-2 Community-Acquired Pneumonia

BACKGROUND

Hospitalized patients with SARS-CoV-2 community-acquired pneumonia (CAP) and associated comorbidities are at increased risk of cardiovascular complications. The magnitude of effect of cardiovascular complications and the role of prior comorbidities on clinical outcomes are not well defined.

RESEARCH QUESTION

What is the impact of cardiovascular complications on mortality in hospitalized patients with SARS-CoV-2 CAP? What is the impact of comorbidities and other risk factors on the risk of developing cardiovascular complications and mortality in these patients?

STUDY DESIGN AND METHODS

This cohort study included 1,645 hospitalized patients with SARS-CoV-2 CAP. Cardiovascular complications were evaluated. The clinical course during hospitalization was described by using a multistate model with four states: (1) hospitalized with no cardiovascular complications; (2) hospitalized with cardiovascular complications; (3) discharged alive; (4) and dead. Cox proportional hazards regression was used to analyze the impact of prior comorbid conditions on transitions between these states. Hazard ratios (HRs) and 95% CIs are reported.

RESULTS

Cardiovascular complications occurred in 18% of patients hospitalized with SARS-CoV-2 CAP. The mortality rate in this group was 45% vs 13% in patients without cardiovascular complications. Male subjects (HR, 1.32; 95% CI, 1.03-1.68), older adults (HR, 1.34; 95% CI, 1.03-1.75), and patients with congestive heart failure (HR, 1.59; 95% CI, 1.18-2.15), coronary artery disease (HR, 1.34; 95% CI, 1.00-1.79), atrial fibrillation (HR, 1.43; 95% CI, 1.06-1.95), direct admissions to the ICU (HR, 1.77; 95% CI, 1.36-2.32), and Pao2/Fio2 < 200 (HR, 1.46; 95% CI, 1.11-1.92) were more likely to develop cardiovascular complications following hospitalization for SARS-CoV-2 CAP; however, these factors are not associated with increased risk of death following a cardiovascular complication.

INTERPRETATION

Prior comorbidities, older age, male sex, severity of illness, and hypoxemia are associated with increased risk of cardiovascular complications. Once patients develop cardiovascular complications, the risk of death is extremely high. Cardiovascular complications are the primary drivers of mortality in hospitalized patients with SARS-CoV-2 CAP.

Population mortality in advanced melanoma patients with and without response and progression; data from the Dutch Melanoma Treatment Registry

INTRODUCTION

When analysing patient survival, one is often interested in cause of death. Little is known about the presence of population mortality in advanced melanoma patients. The aim of this study was to assess population mortality after different response states in advanced melanoma patients in the Netherlands, and analyse the contribution of disease and population mortality for different age groups.

METHODS

We selected patients diagnosed between 2013 and 2019 with unresectable IIIC or stage IV melanoma, registered in the Dutch Melanoma Treatment Registry. A multi-state model with response states integrating population mortality was fitted. One-year landmark analyses were performed to assess outcomes after each response state.

RESULTS

Overall, 5119 patients were selected. Five-year probabilities of melanoma-related mortality in patients alive in complete response at one year after diagnosis increased with age, and was 17.2% (95% confidence interval: 13.0-21.4) for patients aged <65 years and 28.7% (95% confidence interval: 24.3-33.1) in patients aged ≥80 years. Population mortality only played a large role for older patients (75 years and above) alive at 1 year after diagnosis with a partial or complete response.

CONCLUSION

Even though survival outcomes of advanced melanoma patients have improved over the last decade, the vast majority of patients still die due to melanoma-related mortality.

Determining the impact of ex-vivo lung perfusion on hospital costs for lung transplantation: A retrospective cohort study

INTRODUCTION

Ex-vivo lung perfusion (EVLP) has improved organ utilization for lung transplantation, but it is not yet known whether the benefits of this technology offset its additional costs. We compared the institutional costs of lung transplantation before vs after EVLP was available to identify predictors of costs and determine the health-economic impact of EVLP.

METHODS

We performed a retrospective, before-after, propensity-score weighted cohort study of patients wait-listed for lung transplant at University Health Network (UHN) in Ontario, Canada, between January 2005 and December 2019 using institutional administrative data. We compared costs, in 2019 Canadian Dollars ($), between patients referred for transplant before EVLP was available (Pre-EVLP) to after (Modern EVLP). Cumulative costs were estimated using a novel application of multistate survival models. Predictors of costs were identified using weighted log-gamma generalized linear regression.

RESULTS

A total of 1,199 patients met inclusion criteria (352 Pre-EVLP; 847 Modern EVLP). Mean total costs for the transplant hospitalization were $111,878 ($94,123-$130,767) in the Pre-EVLP era and $110,969 ($87,714-$136,000) in the Modern EVLP era. Cumulative five-year costs since referral were $278,777 ($82,575-$298,135) in the Pre-EVLP era and $293,680 ($252,832-$317,599) in the Modern EVLP era. We observed faster progression to transplantation when EVLP was available. EVLP availability was not a predictor of waitlist (cost ratio [CR] 1.04 [0.81-1.37]; p = 0.354) or transplant costs (CR 1.02 [0.80-1.29]; p = 0.425) but was associated with lower costs during posttransplant years 1&2 (CR 0.75 [0.58-1.06]; p = 0.05) and posttransplant years 3+ (CR 0.43 [0.26-0.74]; p = 0.001).

CONCLUSIONS

At our center, EVLP availability was associated with faster progression to transplantation at no significant marginal cost.

Ambient air pollution associated with incidence and progression trajectory of cardiometabolic diseases: A multi-state analysis of a prospective cohort

BACKGROUND

Previous studies on the association between ambient air pollution and cardiometabolic diseases (CMDs) focused on a single disease, without considering cardiometabolic multimorbidity (CMM) and the progression trajectory of CMDs.

METHODS

Based on the UK Biobank cohort, we included 372,530 participants aged 37-73 years at baseline (2006-2010) with follow-up until September 2021. Incident CMDs cases were identified based on self-reported information and multiple health-related records in the UK Biobank. CMM was defined as the occurrence of at least two CMDs, including ischemic heart disease (IHD), stroke and type 2 diabetes (T2D). Exposure to ambient air pollutants, including particulate matter (PM) with aerodynamic diameter ≤2.5 μm (PM_2.5), ≤10 μm (PM₁₀), nitrogen dioxide (NO₂), and nitrogen oxides (NO_x) were estimated at participants' geocoded residential addresses based on the high-resolution (1 × 1 km) pollution data from 2001 to 2021 provided by UK Department for Environment, Food and Rural Affairs. Multi-state models with adjustment for potential confounders were used to examine the impact of long-term exposure to ambient air pollution on transitions from healthy to first CMD (FCMD), subsequently to CMM, and further to death.

RESULTS

During a median follow-up of 12.6 years, 40,112 participants developed at least one CMD, 3896 developed CMM, and 21,739 died. Among the four pollutants, PM_2.5 showed the strongest associations with all transitions from healthy to FCMD, to CMM, and then to death [hazard ratios (95 % confidence intervals) per interquartile range (IQR) increment: 1.62 (1.60, 1.64) and 1.68 (1.61, 1.76) for transitions from healthy to FCMD and from FCMD to CMM, and 1.62 (1.59, 1.66), 1.67 (1.61, 1.73), and 1.52 (1.38, 1.67) for death risk from healthy, FCMD, and CMM, respectively]. After dividing FCMDs into three specific CMDs, we found that ambient air pollution had differential impacts on disease-specific transitions within the same transition phase.

CONCLUSIONS

Our findings indicate that there is potential for air pollution mitigation in contributing to the prevention of the development and progression of CMDs.

Associations between multimorbidity and frailty transitions among older Americans

BACKGROUND

The associations of multimorbidity patterns with transitions between frailty states remain unclear in older individuals.

METHODS

We used data from the National Health and Aging Trends Study 2011-2019. Frailty was measured annually using the Fried frailty phenotype. Multimorbidity patterns at baseline were identified using latent class analysis based on 14 chronic conditions. We used the semi-Markov multi-state model to investigate the influences of multimorbidity characterized by condition counts and patterns on subsequent frailty transitions over follow-ups.

RESULTS

Among 9450 participants aged ≥65 years at baseline, 34.8% were non-frail, 48.1% were pre-frail and 17.0% were frail. Over a median follow-up of 4.0 years, 16 880 frailty transitions were observed, with 10 527 worsening and 6353 improving. For 7675 participants with multimorbidity, four multimorbidity patterns were identified: osteoarticular pattern (62.4%), neuropsychiatric-sensory pattern (17.2%), cardiometabolic pattern (10.3%) and complex multimorbidity pattern (10.1%). Compared with no disease, multimorbidity was significantly associated with an increased risk of worsening transitions, including from non-frail to pre-frail (hazard ratio [HR] = 1.35; 95% confidence interval [CI] = 1.21-1.52), from non-frail to frail (HR = 1.68; 95% CI = 1.04-2.73), from pre-frail to frail (HR = 2.19; 95% CI = 1.66-2.90) and from pre-frail to death (HR = 1.64; 95% CI = 1.11-2.41). Compared with the osteoarticular pattern, neuropsychiatric-sensory, cardiometabolic and complex multimorbidity patterns had a significantly higher risk of worsening frailty (all P < 0.05).

CONCLUSIONS

Multimorbidity was associated with dynamic transitions between frailty states and death among older American adults, and the associations varied across multimorbidity patterns. The findings could offer significant implications for public health policymakers in planning interventions and healthcare resources. They also might inform clinicians regarding providing targeted clinical treatment and health management based on multimorbidity patterns of older people.

Development and validation of a decision model for the evaluation of novel lung cancer treatments in the Netherlands

Recent discoveries in molecular diagnostics and drug treatments have improved the treatment of patients with advanced (inoperable) non-squamous non-small cell lung cancer (NSCLC) from solely platinum-based chemotherapy to more personalized treatment, including targeted therapies and immunotherapies. However, these improvements come at considerable costs, highlighting the need to assess their cost-effectiveness in order to optimize lung cancer care. Traditionally, cost-effectiveness models for the evaluation of new lung cancer treatments were based on the findings of the randomized control trials (RCTs). However, the strict RCT inclusion criteria make RCT patients not representative of patients in the real-world. Patients in RCTs have a better prognosis than patients in a real-world setting. Therefore, in this study, we developed and validated a diagnosis-treatment decision model for patients with advanced (inoperable) non-squamous NSCLC based on real-world data in the Netherlands. The model is a patient-level microsimulation model implemented as discrete event simulation with five health events. Patients are simulated from diagnosis to death, including at most three treatment lines. The base-model (non-personalized strategy) was populated using real-world data of patients treated with platinum-based chemotherapy between 2008 and 2014 in one of six Dutch teaching hospitals. To simulate personalized care, molecular tumor characteristics were incorporated in the model based on the literature. The impact of novel targeted treatments and immunotherapies was included based on published RCTs. To validate the model, we compared survival under a personalized treatment strategy with observed real-world survival. This model can be used for health-care evaluation of personalized treatment for patients with advanced (inoperable) NSCLC in the Netherlands.

Multistate modeling and structure selection for multitype recurrent events and terminal event data

In cardiovascular disease studies, a large number of risk factors are measured but it often remains unknown whether all of them are relevant variables and whether the impact of these variables is changing with time or remains constant. In addition, more than one kind of cardiovascular disease events can be observed in the same patient and events of different types are possibly correlated. It is expected that different kinds of events are associated with different covariates and the forms of covariate effects also vary between event types. To tackle these problems, we proposed a multistate modeling framework for the joint analysis of multitype recurrent events and terminal event. Model structure selection is performed to identify covariates with time-varying coefficients, time-independent coefficients, and null effects. This helps in understanding the disease process as it can detect relevant covariates and identify the temporal dynamics of the covariate effects. It also provides a more parsimonious model to achieve better risk prediction. The performance of the proposed model and selection method is evaluated in numerical studies and illustrated on a real dataset from the Atherosclerosis Risk in Communities study.

The linkage of depressive and anxiety disorders with the expected labor market affiliation (ELMA): a longitudinal multi-state study of Danish employees

OBJECTIVE

Depressive and anxiety disorders are prevalent among employees in general. Still, knowledge regarding the contribution of these disorders to the dynamics of the labor market in terms of working time, sickness absence, and unemployment is scarce. We aim to quantify the linkage of depressive and anxiety disorders with labor market participation using the expected labor market affiliation method (ELMA), in a large sample of Danish employees.

METHODS

We combined three survey waves on occupational health with six high-quality national registers in N = 43,148 Danish employees, of which the 2012 survey contributed 29,665 person years, the 2014 survey 33,043 person years, and the 2016 survey 35,375 person years. We used the new ELMA method to estimate the multi-state transition probabilities and 2-year expected time in work, sickness absence, and unemployment. Depressive and anxiety disorders were assessed by the Major Depression Inventory and the SCL-ANX4 scales, respectively. We adjusted for multiple variables by applying inverse probability weighting in groups of gender and age.

RESULTS

Depressive and anxiety disorders among employees link to reduced labor market affiliation by significantly changed transitions probabilities between the labor markets states, viewed as reduced working time by 4-51 days (in two years), increased time in sickness absence by 6-44 days (in two years), and unemployment by 6-12 days (in two years) when compared to employees without depression or anxiety disorders. The results were most pronounced for women employees and for employees with both depression and anxiety disorders.

CONCLUSIONS

The study reveals detailed insight into what extent depression and anxiety disorders influence the labor market affiliation, in terms of the complex interrelation between working time, sickness absence, and unemployment. The study emphasizes the importance of preventing and handling depressive and anxiety disorders among employees for strengthening work participation.

Association of Oxygen Therapy with the Natural Disease Progression of Cystic Fibrosis: A Multi-State Model of the European Cystic Fibrosis Society Patient Registry

Background

Association between dependence on oxygen therapy (OT) and natural disease progression in people with cystic fibrosis (pwCF) has not been estimated yet. The aim of this study is to understand the prognosis for pwCF on OT, evaluating how the transition probabilities from being alive without lung transplantation (LTx) to LTx and to death, and from being alive after LTx to death change in pwCF with and without OT.

Methods

We used 2008-2017 data from the 35-country European CF Society Patient Registry. A multi-state model was fitted to assess the effects of individual risk factors on transition probabilities.

Results

We considered 48,343 pwCF aged from 6 to 50 years. OT (HR 5.78, 95% CI: 5.32-6.29) and abnormal FEV₁ (HR 6.41, 95% CI: 5.28-7.79) were strongly associated with the probability of having LTx; chronic infection with Burkholderia cepacia complex (HR 3.19, 95% CI: 2.78-3.67), abnormal FEV₁ (HR 5.00, 95% CI: 4.11-6.08) and the need for OT (HR 4.32, 95% CI: 3.93-4.76) showed the greatest association with the probability of dying without LTx. Once pwCF received LTx, OT (HR 1.75, 95% CI: 1.41-2.16) and abnormal FEV₁ (HR 1.63, 95% CI: 1.18-2.25) were the main factors associated with the probability of dying. An association of gross national income with the probability of receiving LTx and with the probability of dying without LTx was also found.

Conclusion

Oxygen therapy is associated with poor survival in pwCF with and without LTx; harmonization of CF care throughout European countries and minimization of the onset of pulmonary gas exchange abnormalities using all available means remains of paramount importance.

Ten-Year Probabilities of Death Due to Cancer and Cardiovascular Disease among Breast Cancer Patients Diagnosed in North-Eastern Spain

Mortality from cardiovascular disease (CVD), second tumours, and other causes is of clinical interest in the long-term follow-up of breast cancer (BC) patients. Using a cohort of BC patients (N = 6758) from the cancer registries of Girona and Tarragona (north-eastern Spain), we studied the 10-year probabilities of death due to BC, other cancers, and CVD according to stage at diagnosis and hormone receptor (HR) status. Among the non-BC causes of death (N = 720), CVD (N = 218) surpassed other cancers (N = 196). The BC cohort presented a significantly higher risk of death due to endometrial and ovarian cancers than the general population. In Stage I, HR- patients showed a 1.72-fold higher probability of all-cause death and a 6.11-fold higher probability of breast cancer death than HR+ patients. In Stages II-III, the probability of CVD death (range 3.11% to 3.86%) surpassed that of other cancers (range 0.54% to 3.11%). In Stage IV patients, the probability of death from any cancer drove the mortality risk. Promoting screening and preventive measures in BC patients are warranted, since long-term control should encompass early detection of second neoplasms, ruling out the possibility of late recurrence. In patients diagnosed in Stages II-III at an older age, surveillance for preventing late cardiotoxicity is crucial.

Role of depression in the development of cardiometabolic multimorbidity: Findings from the UK Biobank study

BACKGROUND

Depression has been acknowledged as a risk factor for cardiometabolic diseases, but its role in the development of cardiometabolic multimorbidity (CM) remains unclear. We aimed to prospectively investigate how depression affected the development of CM based on the UK Biobank study.

METHODS

We included 459,747 participants with none or one prior cardiometabolic disease. Depression was assessed by the clinical diagnosis and Patient Health Questionnaire (PHQ-2). CM was defined as the coexistence of two or more conditions of type 2 diabetes, stroke, and coronary heart disease (CHD). Multistate models were used to examine the role of depression in the transitions from baseline to single cardiometabolic diseases and subsequently to CM.

RESULTS

During a median follow-up of 12.07 years, we documented 3413 incident CM cases among initially disease-free participants and 7461 cases among participants with one prior cardiometabolic disease, respectively. The hazard of developing CM associated with depression was higher among disease-free individuals than that among individuals with one cardiometabolic disease (HR, 95 % CI: 1.68, 1.54-1.83 vs 1.28, 1.20-1.35). Moreover, depression was significantly associated with all transitions from baseline to diabetes (HR, 95 % CI: 1.43, 1.37-1.50), stroke (1.28, 1.20-1.38), and CHD (1.35, 1.31-1.40). After the onset of these cardiometabolic diseases, the HR values (95 % CIs) of progression to CM were 1.26 (1.09-1.46) for diabetes, 1.43 (1.16-1.76) for stroke, and 1.23 (1.08-1.40) for CHD.

CONCLUSIONS

Depression was an independent risk factor for CM, and adversely influenced the whole progression from disease-free status to CM.

The role of type 2 diabetes in the association between habitual glucosamine use and dementia: a prospective cohort study

BACKGROUND

Growing evidence has showed an association between habitual glucosamine use and type 2 diabetes (T2D). However, the effect of habitual glucosamine use on risk of dementia remains poorly understood. Our study aimed to examine the association between glucosamine use and risk of dementia and further to identify the mediating role of T2D in the association.

METHODS

A total of 495,942 participants from UK Biobank who completed a questionnaire on habitual glucosamine use were included at baseline (2006-2010) and then followed up for incidence of dementia until 2020. Cox proportional hazard regressions were performed to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident dementia. Markov multi-state models were used to explore the role of incidence of T2D during the follow-up in the association.

RESULTS

Overall, 18.80% of the participants reported habitual use of glucosamine at baseline. A total of 6831 dementia events were recorded during a median follow-up of 11 years. In fully adjusted models, habitual glucosamine use was associated with a significantly lower risk of dementia (HR = 0.87, 95% CI: 0.82-0.93). Multi-state models showed that the association between glucosamine use and dementia was mediated by the incidence of T2D during the follow-up (HR of dementia without T2D: 0.92, 95% CI: 0.86-0.99; HR of post-T2D dementia: 0.79, 95% CI: 0.67-0.93).

CONCLUSIONS

Our findings reveal that habitual use of glucosamine supplement is associated with a lower risk of dementia, which might be explained by incidence of T2D.

Multistate Modeling of Clinical Trajectories and Outcomes in the ICU: A Proof-of-Concept Evaluation of Acute Kidney Injury Among Critically Ill Patients With COVID-19

Multistate models yield high-fidelity analyses of the dynamic state transition and temporal dimensions of a clinical condition's natural history, offering superiority over aggregate modeling techniques for addressing these types of problems.

OBJECTIVES

To demonstrate the utility of these models in critical care, we examined acute kidney injury (AKI) development, progression, and outcomes in COVID-19 critical illness through multistate analyses.

DESIGN SETTING AND PARTICIPANTS

Retrospective cohort study at an urban tertiary-care academic hospital in the United States. All patients greater than or equal to 18 years in an ICU with COVID-19 in 2020, excluding patients with preexisting end-stage renal disease.

MAIN OUTCOMES AND MEASURES

Using electronic health record data, we determined AKI presence/stage in discrete 12-hour time windows and fit multistate models to determine longitudinal transitions and outcomes.

RESULTS

Of 367 encounters, 241 (66%) experienced AKI (maximal stages: 88 stage-1, 49 stage-2, 104 stage-3 AKI [51 received renal replacement therapy (RRT), 53 did not]). Patients receiving RRT overwhelmingly received invasive mechanical ventilation (IMV) (n = 60, 95%) compared with the AKI-without-RRT (n = 98, 53%) and no-AKI groups (n = 39, 32%; p < 0.001), with similar mortality patterns (RRT: n = 36, 57%; AKI: n = 74, 40%; non-AKI: n = 23, 19%; p < 0.001). After 24 hours in the ICU, almost half the cohort had AKI (44.9%; 95% CI, 41.6-48.2%). At 7 days after stage-1 AKI, 74.0% (63.6-84.4) were AKI-free or discharged. By contrast, fewer patients experiencing stage-3 AKI were recovered (30.0% [24.1-35.8%]) or discharged (7.9% [5.2-10.7%]) after 7 days. Early AKI occurred with similar frequency in patients receiving and not receiving IMV: after 24 hours in the ICU, 20.9% of patients (18.3-23.6%) had AKI and IMV, while 23.4% (20.6-26.2%) had AKI without IMV.

CONCLUSIONS AND RELEVANCE

In a multistate analysis of critically ill patients with COVID-19, AKI occurred early and heterogeneously in the course of critical illness. Multistate methods are useful and underused in ICU care delivery science as tools for understanding trajectories, prognoses, and resource needs.

Blood DNA methylation signatures of lifestyle exposures: tobacco and alcohol consumption

BACKGROUND

Smoking and alcohol consumption may compromise health by way of epigenetic modifications. Epigenetic signatures of alcohol and tobacco consumption could provide insights into the reversibility of phenotypic changes incurred with differing levels of lifestyle exposures. This study describes and validates two novel epigenetic signatures of tobacco (EpiTob) and alcohol (EpiAlc) consumption and investigates their association with disease outcomes.

METHODS

The epigenetic signatures, EpiTob and EpiAlc, were developed using data from the Swiss Kidney Project on Genes in Hypertension (SKIPOGH) (N = 689). Epigenetic and phenotypic data available from the 1921 (N = 550) and 1936 (N = 1091) Lothian Birth Cohort (LBC) studies, and two publicly available datasets on GEO Accession (GSE50660, N = 464; and GSE110043, N = 94) were used to validate the signatures. A multivariable logistic regression model, adjusting for age and sex, was used to assess the association between self-reported tobacco or alcohol consumption and the respective epigenetic signature, as well as to estimate the association between CVD and epigenetic signatures. A Cox proportional hazard model was used to estimate the risk of mortality in association with the EpiTob and EpiAlc signatures.

RESULTS

The EpiTob signature was positively associated with self-reported tobacco consumption for current or never smokers with explained variance ranging from 0.49 (LBC1921) to 0.72 (LBC1936) (pseudo-R2). In the SKIPOGH, LBC1921 and LBC1936 cohorts, the epigenetic signature for alcohol consumption explained limited variance in association with self-reported alcohol status [i.e., non-drinker, moderate drinker, and heavy drinker] (pseudo-R2 = 0.05, 0.03 and 0.03, respectively), although this improved considerably when measuring self-reported alcohol consumption with standardized units consumed per week (SKIPOGH R2 = 0.21; LBC1921 R2 = 0.31; LBC1936 R2 = 0.41). Both signatures were associated with history of CVD in SKIPOGH and LBC1936, but not in LBC1921. The EpiTob signature was associated with increased risk of all-cause and lung-cancer specific mortality in the 1936 and 1921 LBC cohorts.

CONCLUSIONS

This study found the EpiTob and EpiAlc signatures to be well-correlated with self-reported exposure status and associated with long-term health outcomes. Epigenetic signatures of lifestyle exposures may reduce measurement issues and biases and could aid in risk stratification for informing early-stage targeted interventions.

Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model

Objectives

To adopt a multi-state risk prediction model for critical disease/mortality outcomes among hospitalised COVID-19 patients using nationwide COVID-19 hospital surveillance data in Belgium.

Materials and methods

Information on 44,659 COVID-19 patients hospitalised between March 2020 and June 2021 with complete data on disease outcomes and candidate predictors was used to adopt a multi-state, multivariate Cox model to predict patients' probability of recovery, critical [transfer to intensive care units (ICU)] or fatal outcomes during hospital stay.

Results

Median length of hospital stay was 9 days (interquartile range: 5-14). After admission, approximately 82% of the COVID-19 patients were discharged alive, 15% of patients were admitted to ICU, and 15% died in the hospital. The main predictors of an increased probability for recovery were younger age, and to a lesser extent, a lower number of prevalent comorbidities. A patient's transition to ICU or in-hospital death had in common the following predictors: high levels of c-reactive protein (CRP) and lactate dehydrogenase (LDH), reporting lower respiratory complaints and male sex. Additionally predictors for a transfer to ICU included middle-age, obesity and reporting loss of appetite and staying at a university hospital, while advanced age and a higher number of prevalent comorbidities for in-hospital death. After ICU, younger age and low levels of CRP and LDH were the main predictors for recovery, while in-hospital death was predicted by advanced age and concurrent comorbidities.

Conclusion

As one of the very few, a multi-state model was adopted to identify key factors predicting COVID-19 progression to critical disease, and recovery or death.