Disease progression in chronic heart failure is linear: Insights from multistate modelling.

AIMS: Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF. METHODS AND RESULTS: Consecutive patients (n = 4918) were enrolled (median age 75 [67-81] years, 61.3% men, 44% with HF and reduced ejection fraction). We generated a model by observing events during the first 2 years of follow-up. The model yielded surprisingly accurate predictions of how a population with HF will behave during subsequent years. As examples, the predicted transition probability from hospitalization to death was 0.11; the observed probabilities were 0.13, 0.14, and 0.16 at 3, 4, and 5 years, respectively. Similarly, the predicted transition intensity for rehospitalization was 0.35; the observed probabilities were 0.38, 0.34, and 0.35 at 3, 4, and 5 years, respectively. A multivariable model including covariates thought to influence outcome did not improve accuracy. Predicted average life expectancy was approximately 10 years for the unadjusted model and 13 years for the multivariable model, consistent with the observed mortality of 41% at 5 years. CONCLUSIONS: A multistate Markov chain model for patients with chronic HF suggests that the proportion of patients transitioning each year from a given state to another remains constant. This finding suggests that the course of HF at a population level is more linear than is commonly supposed and predictable based on current patient status.

Transitions in intrinsic capacity among community-dwelling older people and their associated factors: a multistate modelling analysis.

OBJECTIVES: Trajectory of intrinsic capacity (IC) can be non-linear and discontinuous, which traditional linear models may not be able to handle. This study thus aimed to model the trajectory of IC as transitions between different IC states and examine their associated factors. METHODS: Longitudinal data from a sample of community-dwelling older people aged 60 years or above (n = 1,588) was analysed. A set of 14 self-reported items representing different domains of IC were administered annually to measure IC at four time points. Based on the number of impaired IC domains (i.e., cognitive, locomotor, vitality, sensory, and psychological), participants at each time point were classified into one of three IC states, namely state 1 (0 impaired domain), state 2 (1-2 impaired domains), and state 3 (3-5 impaired domains). Multistate modelling was used to identify factors associated with the transitions from one state to another. RESULTS: The mean age of participants was 75.0 years, and 77.4% of them were female. At baseline, 12.4% were in state 1, 51.8% were in state 2, and 35.8% were in state 3. 62.8% of participants experienced at least one transition between states, among which 12% experienced a transition every year. The transitions occurred mostly between adjacent IC states and could take place back and forth. Age, sex, marital status, perceived financial adequacy, number of chronic diseases, and self-rated health were the factors associated with the transitions. CONCLUSION: Findings may serve as a valuable reference for guiding future policies to optimize IC and promote healthy ageing using a person-centred approach.

Mate-guarding success depends on male investment in a butterfly.

Males of many insects, including butterflies, produce mate-guarding devices, such as mating plugs, to prolong guarding and prevent future female matings in the male's absence. In a few butterflies, large external mate-guarding devices, that is, sphragides, occur. Gór et al. (Behaviour, 160, 2023 and 515-557) found conspicuously large size and morphological variation of mate-guarding devices within a single population of the potentially polyandrous Clouded Apollo (Parnassius mnemosyne, L.) butterfly. They termed the externally visible male-produced devices as Copulatory opening APpendices (CAP) consisting of small devices, termed small CAPs and the much larger shield (i.e. sphragis). Our aim was to reveal CAP replacement dynamics within females during their lifetime and to understand how male investment into small CAPs or shields was (i) related to CAP persistence on the female, that is securing paternity, (ii) associated with female quality, measured as size and (iii) with actual adult sex ratio. We investigated a univoltine Clouded Apollo population to estimate CAP replacement risks, using multistate survival models, in an extensive observational study through 6 years based on mark-recapture. Shields were the most frequent mate-guarding devices and were more persistent than small CAPs, often lasting for life, excluding future matings. Thus, most females bearing a shield were deprived of postcopulatory female choice, and the genetic variance in their offspring could be reduced compared to those bearing small CAPs, thus mating more often. The ratio of shields to all CAPs gradually decreased towards the end of the flight period. Males were more prone to produce a shield when mating females with wider thoraces and when the ratio of males (i.e. competition) was higher in the population. To our best knowledge, this is the first quantitative study to investigate potential factors on which male investment in mate-guarding devices may depend, and how the variation in these devices impacts CAP persistence on females.

Resolving domain positions of cellobiose dehydrogenase by small angle X-ray scattering.

The interdomain electron transfer (IET) between the catalytic flavodehydrogenase domain and the electron-transferring cytochrome domain of cellobiose dehydrogenase (CDH) plays an essential role in biocatalysis, biosensors and biofuel cells, as well as in its natural function as an auxiliary enzyme of lytic polysaccharide monooxygenase. We investigated the mobility of the cytochrome and dehydrogenase domains of CDH, which is hypothesised to limit IET in solution by small angle X-ray scattering (SAXS). CDH from Myriococcum thermophilum (syn. Crassicarpon hotsonii, syn. Thermothelomyces myriococcoides) was probed by SAXS to study the CDH mobility at different pH and in the presence of divalent cations. By comparison of the experimental SAXS data, using pair-distance distribution functions and Kratky plots, we show an increase in CDH mobility at higher pH, indicating alterations of domain mobility. To further visualise CDH movement in solution, we performed SAXS-based multistate modelling. Glycan structures present on CDH partially masked the resulting SAXS shapes, we diminished these effects by deglycosylation and studied the effect of glycoforms by modelling. The modelling shows that with increasing pH, the cytochrome domain adopts a more flexible state with significant separation from the dehydrogenase domain. On the contrary, the presence of calcium ions decreases the mobility of the cytochrome domain. Experimental SAXS data, multistate modelling and previously reported kinetic data show how pH and divalent ions impact the closed state necessary for the IET governed by the movement of the CDH cytochrome domain.

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.

Effects of Antiretroviral Therapy on CD4+ Cell Count, HIV Viral Load and Death in a South African Cohort: A Modelling Study.

BACKGROUND AND OBJECTIVE: Combination antiretroviral therapy (cART) has improved the survival of HIV infected patients significantly. However, in some patients, survival is not guaranteed due to several factors that are either individual-based or cART based. This study presents an HIV, AIDS, Death (HAD) model to analyse the survival of patients on cART. MATERIALS AND METHODS: Continuous-time Markov models are fitted based on the states occupied for an HIV, AIDS and Death (HAD) model. These states are based on CD4 cell count. Factors that affect the survival of HIV-infected patients on cART are also analyzed. These, among others, include age, gender, routinely collected viral load, time on treatment, non-adherence and peripheral neuropathy. RESULTS: Patients with higher viral loads than expected are 11.1 times more likely to be at risk of HIV progression to the AIDS state and 1.1 times more likely to be at risk of mortality from a CD4 cell count state above 200 cell/mm3compared to patients with lower viral loads. Non-adherence to treatment increases the risk of transition from CD4 cell count state above 200 cell/mm3 to the AIDS state by 2.2 folds. Patients who were non-adherent to treatment are 3.8 times more likely to transit from the CD4 state above 200 cell/mm3 to death compared to patients who were adherent to treatment. Patients are expected to recover from the AIDS state after one year of treatment. CONCLUSIONS: Recovery from AIDS state by HIV infected patients on cART is likely to occur after one year of cART treatment. However, if the viral load remains higher than expected, this increases risks of immune deterioration even after having achieved normal CD4 cell counts and consequently, mortality risks are increased.

A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy.

BACKGROUND: CD4 cell count has been identified to be an essential component in monitoring HIV treatment outcome. However, CD4 cell count monitoring sometimes fails to predict virological failure resulting in unnecessary switch of treatment lines which causes drug resistance and limitations of treatment options. This study assesses the use of both viral load (HIV RNA) and CD4 cell count in the monitoring of HIV/AIDS progression. METHODS: Time-homogeneous Markov models were fitted, one on CD4 cell count monitoring and the other on HIV RNA monitoring. Effects of covariates; gender, age, CD4 baseline, HIV RNA baseline and adherence to treatment were assessed for each of the fitted models. Assessment of the fitted models was done using prevalence plots and the likelihood ratio tests. The analysis was done using the "msm" package in R. RESULTS: Results from the analysis show that viral load monitoring predicts deaths of HIV/AIDS patients better than CD4 cell count monitoring. Assessment of the fitted models shows that viral load monitoring is a better predictor of HIV/AIDS progression than CD4 cell count. CONCLUSION: From this study one can conclude that although patients take more time to achieve a normal CD4 cell count and less time to achieve an undetectable viral load, once the CD4 cell count is normal, mortality risks are reduced. Therefore, both viral load monitoring and CD4 count monitoring can be used to provide useful information which can be used to improve life expectance of patients living with HIV. However, viral load monitoring is a better predictor of HIV/AIDS progression than CD4 cell count and hence viral load is deemed superior.

Multistate modelling to estimate excess length of stay and risk of death associated with organ/space infection after elective colorectal surgery.

BACKGROUND: Accounting for time-dependency and competing events are strongly recommended to estimate excess length of stay (LOS) and risk of death associated with healthcare-associated infections. AIM: To assess the effect of organ/space (OS) surgical site infection (SSI) on excess LOS and in-hospital mortality in patients undergoing elective colorectal surgery (ECS). METHODS: A multicentre prospective adult cohort undergoing ECS, January 2012 to December 2014, at 10 Spanish hospitals was used. SSI was considered the time-varying exposure and defined as incisional (superficial and deep) or OS. Discharge alive and death were the study endpoints. The mean excess LOS was estimated using a multistate model which provided a weighted average based on the states patients passed through. Multivariate Cox regression models were used to assess the effect of OS-SSI on risk of discharge alive or in-hospital mortality. FINDINGS: Of 2778 patients, 343 (12.3%) developed SSI: 194 (7%) OS-SSI and 149 (5.3%) incisional SSI. Compared to incisional SSI or no infection, OS-SSI prolonged LOS by 4.2 days (95% confidence interval (CI): 4.1-4.3) and 9 days (8.9-9.1), respectively, reduced the risk of discharge alive (adjusted hazard ratio (aHR): 0.36 (95% CI: 0.28-0.47) and aHR: 0.17 (0.14-0.21), respectively), and increased the risk of in-hospital mortality (aHR: 8.02 (1.03-62.9) and aHR: 10.7 (3.7-30.9), respectively). CONCLUSION: OS-SSI substantially extended LOS and increased risk of death in patients undergoing ECS. These results reinforce OS-SSI as the SSI with the highest health burden in ECS.

Multistate modelling extended by behavioural rules: An application to migration.

We propose to extend demographic multistate models by adding a behavioural element: behavioural rules explain intentions and thus transitions. Our framework is inspired by the Theory of Planned Behaviour. We exemplify our approach with a model of migration from Senegal to France. Model parameters are determined using empirical data where available. Parameters for which no empirical correspondence exists are determined by calibration. Age- and period-specific migration rates are used for model validation. Our approach adds to the toolkit of demographic projection by allowing for shocks and social influence, which alter behaviour in non-linear ways, while sticking to the general framework of multistate modelling. Our simulations yield that higher income growth in Senegal leads to higher emigration rates in the medium term, while a decrease in fertility yields lower emigration rates.