Handling missing disease information due to death in diseases that need two visits to diagnose.

In studies that assess disease status periodically, time of disease onset is interval censored between visits. Participants who die between two visits may have unknown disease status after their last visit. In this work, we consider an additional scenario where diagnosis requires two consecutive positive tests, such that disease status can also be unknown at the last visit preceding death. We show that this impacts the choice of censoring time for those who die without an observed disease diagnosis. We investigate two classes of models that quantify the effect of risk factors on disease outcome: a Cox proportional hazards model with death as a competing risk and an illness death model that treats disease as a possible intermediate state. We also consider four censoring strategies: participants without observed disease are censored at death (Cox model only), the last visit, the last visit with a negative test, or the second last visit. We evaluate the performance of model and censoring strategy combinations on simulated data with a binary risk factor and illustrate with a real data application. We find that the illness death model with censoring at the second last visit shows the best performance in all simulation settings. Other combinations show bias that varies in magnitude and direction depending on the differential mortality between diseased and disease-free subjects, the gap between visits, and the choice of the censoring time.

Two biases in incubation time estimation related to exposure.

BACKGROUND: Estimation of the SARS-CoV-2 incubation time distribution is hampered by incomplete data about infection. We discuss two biases that may result from incorrect handling of such data. Notified cases may recall recent exposures more precisely (differential recall). This creates bias if the analysis is restricted to observations with well-defined exposures, as longer incubation times are more likely to be excluded. Another bias occurred in the initial estimates based on data concerning travellers from Wuhan. Only individuals who developed symptoms after their departure were included, leading to under-representation of cases with shorter incubation times (left truncation). This issue was not addressed in the analyses performed in the literature. METHODS: We performed simulations and provide a literature review to investigate the amount of bias in estimated percentiles of the SARS-CoV-2 incubation time distribution. RESULTS: Depending on the rate of differential recall, restricting the analysis to a subset of narrow exposure windows resulted in underestimation in the median and even more in the 95th percentile. Failing to account for left truncation led to an overestimation of multiple days in both the median and the 95th percentile. CONCLUSION: We examined two overlooked sources of bias concerning exposure information that the researcher engaged in incubation time estimation needs to be aware of.

A novel diagnostic model for tuberculous meningitis using Bayesian latent class analysis.

BACKGROUND: Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known. METHODS: We included 659 individuals aged [Formula: see text] years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl-Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally. RESULTS: Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden's Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively. CONCLUSION: Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research.

Dengue viremia kinetics and effects on platelet count and clinical outcomes: An analysis of 2340 patients from Vietnam.

Background: Viremia is a critical factor in understanding the pathogenesis of dengue infection, but limited data exist on viremia kinetics. This study aimed to investigate the kinetics of viremia and its effects on subsequent platelet count, severe dengue, and plasma leakage. Methods: We pooled data from three studies conducted in Vietnam between 2000 and 2016, involving 2340 dengue patients with daily viremia measurements and platelet counts after symptom onset. Viremia kinetics were assessed using a random effects model that accounted for left-censored data. The effects of viremia on subsequent platelet count and clinical outcomes were examined using a landmark approach with a random effects model and logistic regression model with generalized estimating equations, respectively. The rate of viremia decline was derived from the model of viremia kinetics. Its effect on the clinical outcomes was assessed by logistic regression models. Results: Viremia levels rapidly decreased following symptom onset, with variations observed depending on the infecting serotype. DENV-1 exhibited the highest mean viremia levels during the first 5-6 days, while DENV-4 demonstrated the shortest clearance time. Higher viremia levels were associated with decreased subsequent platelet counts from day 6 onwards. Elevated viremia levels on each illness day increased the risk of developing severe dengue and plasma leakage. However, the effect size decreased with later illness days. A more rapid decline in viremia is associated with a reduced risk of the clinical outcomes. Conclusions: This study provides comprehensive insights into viremia kinetics and its effect on subsequent platelet count and clinical outcomes in dengue patients. Our findings underscore the importance of measuring viremia levels during the early febrile phase for dengue studies and support the use of viremia kinetics as outcome for phase-2 dengue therapeutic trials. Funding: Wellcome Trust and European Union Seventh Framework Programme.

Metformin as adjunctive therapy in combination with multidrug treatment for multibacillary leprosy: A protocol for a randomized double-blind, controlled Phase 2 trial in Indonesia (MetLep Trial).

Background: The clinical management of leprosy is complicated by leprosy reactions (LR) causing irreversible nerve damage and disabilities. LR often require long-term use of corticosteroids causing serious side effects. Adjunct host-directed therapy (HDT) is a potentially attractive strategy in leprosy to prevent LR and associated immunopathology, modulate immunological memory that protects against recurrence, and thereby reduce nerve damage, disability and corticosteroid-associated morbidities. Metformin, a well-tolerated, safe and cheap anti-hyperglycaemic drug, is repurposed as HDT in auto-immune and infectious diseases, like tuberculosis (TB). Metformin use in people with diabetes is associated with reduced risks of TB-infection, progression to active TB, treatment failure and TB-mortality. Given the similarities both mycobacteria share, we hypothesize that among persons with multibacillary (MB) leprosy, adjunctive metformin may prevent/mitigate LR. Methods: We will perform a double-blind controlled proof-of-concept trial in which people with newly diagnosed multibacillary leprosy will be randomized (1:1) to metformin hydrochloride 1000mg extended release once daily versus placebo for 24 weeks in addition to standard-of-care WHO MB multidrug therapy (MDT) during 48 weeks. We aim to enrol 166 participants aged between 18 and 65 years, across five clinical sites in two leprosy endemic areas in Indonesia. Primary outcomes are the proportion of participants experiencing a LR and the frequency of (serious) adverse events. Secondary outcomes are the severity and time to first LR, the cumulative corticosteroid usage, and quality of life. The total study follow-up is 48 weeks. Discussion: LR signify the most important cause of irreversible nerve damage leading to anatomical deformities and disabilities, imposing a social and financial burden on those affected. Our study aims to evaluate the efficacy, tolerability and safety of adjunct metformin added to MDT in persons with multibacillary leprosy, and explore its effects on clinical and immunological outcomes. ClinicalTrialsgov registration: NCT05243654 (17/02/2022).

The effect of M. tuberculosis lineage on clinical phenotype.

Six lineages of Mycobacterium tuberculosis sensu stricto (which excludes M. africanum) are described. Single-country or small observational data suggest differences in clinical phenotype between lineages. We present strain lineage and clinical phenotype data from 12,246 patients from 3 low-incidence and 5 high-incidence countries. We used multivariable logistic regression to explore the effect of lineage on site of disease and on cavities on chest radiography, given pulmonary TB; multivariable multinomial logistic regression to investigate types of extra-pulmonary TB, given lineage; and accelerated failure time and Cox proportional-hazards models to explore the effect of lineage on time to smear and culture-conversion. Mediation analyses quantified the direct effects of lineage on outcomes. Pulmonary disease was more likely among patients with lineage(L) 2, L3 or L4, than L1 (adjusted odds ratio (aOR) 1.79, (95% confidence interval 1.49-2.15), p<0.001; aOR = 1.40(1.09-1.79), p = 0.007; aOR = 2.04(1.65-2.53), p<0.001, respectively). Among patients with pulmonary TB, those with L1 had greater risk of cavities on chest radiography versus those with L2 (aOR = 0.69(0.57-0.83), p<0.001) and L4 strains (aOR = 0.73(0.59-0.90), p = 0.002). L1 strains were more likely to cause osteomyelitis among patients with extra-pulmonary TB, versus L2-4 (p = 0.033, p = 0.008 and p = 0.049 respectively). Patients with L1 strains showed shorter time-to-sputum smear conversion than for L2. Causal mediation analysis showed the effect of lineage in each case was largely direct. The pattern of clinical phenotypes seen with L1 strains differed from modern lineages (L2-4). This has implications for clinical management and could influence clinical trial selection strategies.