Timing and Factors Associated With Revision for Infection After Primary Total Knee Arthroplasty Based on American Joint Replacement Registry Data.

BACKGROUND: Infection following total knee arthroplasty (TKA) remains a challenging clinical problem. Using American Joint Replacement Registry data, this study examined factors related to the incidence and timing of infection. METHODS: Primary TKAs performed from January 2012 through December 2018 among patients ≥65 years of age at surgery were queried from the American Joint Replacement Registry and merged with Medicare data to enhance capture of revisions for infection. Multivariate Cox regressions incorporating patient, surgical, and institutional factors were used to produce hazard ratios (HRs) associated with revision for infection and mortality after revision for infection. RESULTS: Among 525,887 TKAs, 2,821 (0.54%) were revised for infection. Men had an increased risk of revision for infection at all-time intervals (≤90 days, HR = 2.06, 95% CI: 1.75-2.43, P < .0001; >90 days to 1 year, HR = 1.90, 95% CI: 1.58-2.28, P < .0001; >1 year, HR = 1.57, 95% CI: 1.37-1.79, P < .0001). TKAs performed for osteoarthritis had an increased risk of revision for infection at ≤90 days (HR = 2.01, 95% CI: 1.45-2.78, P < .0001) but not at later times. Mortality was more likely among patients who had a Charlson Comorbidity Index (CCI) ≥ 5 compared to those who had a CCI ≤ 2 (HR = 3.21, 95% CI: 1.35-7.63, P = .008). Mortality was also more likely among older patients (HR = 1.61 for each decade, 95% CI: 1.04-2.49, P = .03). CONCLUSION: Based on primary TKAs performed in the United States, men were found to have a persistently higher risk of revision for infection, while a diagnosis of osteoarthritis was associated with a significantly higher risk only during the first 90 days after surgery.

The timing and development of infections in a fish-cestode host-parasite system.

The cestode Schistocephalus solidus is a common parasite in freshwater threespine stickleback populations, imposing strong fitness costs on their hosts. Given this, it is surprising how little is known about the timing and development of infections in natural stickleback populations. Previous work showed that young-of-year stickleback can get infected shortly after hatching. We extended this observation by comparing infection prevalence of young-of-year stickleback from 3 Alaskan populations (Walby, Cornelius and Wolf lakes) over 2 successive cohorts (2018/19 and 2019/20). We observed strong variation between sampling years (2018 vs 2019 vs 2020), stickleback age groups (young-of-year vs 1-year-old) and sampling populations.

Factors Associated With the Incidence and Timing of Total Knee Arthroplasty Infection.

BACKGROUND: Periprosthetic joint infection (PJI) following total knee arthroplasty remains a challenging clinical problem. This study examined variables related to the incidence and timing of PJI. METHODS: We retrospectively reviewed 8462 primary total knee arthroplasties performed at our institution between 2006 and 2018 for PJI. The mean follow-up is 3.7 years. Eighty-seven variables including patient-reported diagnoses, demographics, and medications were collected. Time to infection, bacterial organism, success of infection treatment, and variables associated with infection are reported. RESULTS: PJI occurred in 105 (1.24%) cases. The incidence of infection in the first year was 0.72% and represented 58% of all infections. Multivariate Cox regression revealed males (hazard ratio [HR] = 2.85, 95% confidence interval [CI], 1.69-4.79) and patients with major depression or anxiety (HR = 2.11, 95% CI, 1.21-3.67) were more likely to develop an infection in the first year. After the first year, patients with a history of cellulitis (HR = 3.97, 95% CI, 1.91-8.27) and those taking antiepileptic medications (HR = 3.61, 95% CI, 1.73-7.52) were more likely to develop an infection. Culture-negative infections were more common after one year than before (23% vs 8%, P = .04). Debridement, antibiotics, and implant retention was used in 79% (48/61) of infections in the first year and 55% (24/44) after one year with success rates of 51% and 70%, respectively (P = .16). CONCLUSION: The majority of infections occur during the first year after surgery. Importantly, patient variables associated with infection are different among infections that occur before and after one year. A multicenter study with a much larger number of infections may allow analysis of more time intervals after surgery.

Infection timing affects Fusarium poae colonization of bread wheat spikes and mycotoxin accumulation in the grain.

BACKGROUND: Fusarium poae is one of the most common Fusarium head blight (FHB) causal agents in wheat. This species can biosynthesize a wide range of mycotoxins, in particular nivalenol (NIV). In FHB epidemiology, infection timing is important for disease occurrence, kernel development, symptom appearance and mycotoxin accumulation in grain. The present study explored, both in a controlled environment and in a 2-year field plot experiment in Central Italy, the influence of five infection timings (from beginning of flowering to medium milk growth stage) on F. poae colonization and mycotoxin accumulation in bread wheat spikes (spring cv. A416 and winter cv. Ambrogio). RESULTS: Both climate chamber and field experiments showed that early infection timings (from beginning of flowering to full flowering) especially favoured F. poae colonization and accumulation of its mycotoxins (particularly NIV) in grain. By contrast, later infection timings (watery ripe and medium milk) reduced F. poae development and mycotoxin levels. The time window of host susceptibility in the field was shorter than that observed under controlled conditions. Symptom expression in kernels also differed among infection timings. In general, F. poae biomass was higher in the chaff than in the grain. CONCLUSION: These results enhance knowledge of a common member of the FHB complex worldwide, and could be useful in forecasting the risk of F. poae infection and mycotoxin contamination. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Timing of Susceptibility to Fusarium Head Blight in Winter Wheat.

The duration of wheat susceptibility to Fusarium infection has implications for risk forecasting, fungicide timing, and the likelihood that visible kernel damage may underpredict deoxynivalenol (DON) contamination. A field experiment was conducted to explore the impact of varying infection timings on Fusarium head blight (FHB) development in winter wheat. Trials in four successive years (2010 to 2013) in North Carolina utilized one susceptible and one moderately resistant cultivar possessing similar maturity, stature, and grain quality. Inoculum was applied in the form of sprayed Fusarium graminearum conidia. In the first year, the nine infection timings were from 0 to 21 days after anthesis (daa), whereas in the following 3 years, they ranged from 0 to 13 daa. Infection progression was compared among inoculation timings by sampling spikes five to six times during grain-fill. Based on DON, percent kernel damage and kernel infection, and fungal spread as assayed via qPCR, the moderately resistant cultivar had at least a 2- to 3-day shorter window of susceptibility to damaging FHB infection than the susceptible cultivar. The results suggest that duration of susceptibility is an important aspect of cultivar resistance to FHB. In 2012, the window of susceptibility for both cultivars was extended by cold snaps during anthesis. After debranning in one year, the majority of DON was found to be in the bran fraction of kernels; there was also a trend for later infections to lead to a higher percentage of DON in the nonbran fraction, as well as a higher ratio of DON to FDK.

Interpreting HIV diagnostic histories into infection time estimates: analytical framework and online tool.

BACKGROUND: It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the 'Fiebig staging' system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests - as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection. METHODS: The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time 'point estimates' and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice). RESULTS: In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. CONCLUSIONS: This tool, available at https://tools.incidence-estimation.org/idt/ , is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection.

BACKGROUND: There is today no gold standard method to accurately define the time passed since infection at HIV diagnosis. Infection timing and incidence measurement is however essential to better monitor the dynamics of local epidemics and the effect of prevention initiatives. METHODS: Three methods for infection timing were evaluated using 237 serial samples from documented seroconversions and 566 cross sectional samples from newly diagnosed patients: identification of antibodies against the HIV p31 protein in INNO-LIA, SediaTM BED CEIA and SediaTM LAg-Avidity EIA. A multi-assay decision tree for infection timing was developed. RESULTS: Clear differences in recency window between BED CEIA, LAg-Avidity EIA and p31 antibody presence were observed with a switch from recent to long term infection a median of 169.5, 108.0 and 64.5 days after collection of the pre-seroconversion sample respectively. BED showed high reliability for identification of long term infections while LAg-Avidity is highly accurate for identification of recent infections. Using BED as initial assay to identify the long term infections and LAg-Avidity as a confirmatory assay for those classified as recent infection by BED, explores the strengths of both while reduces the workload. The short recency window of p31 antibodies allows to discriminate very early from early infections based on this marker. BED recent infection results not confirmed by LAg-Avidity are considered to reflect a period more distant from the infection time. False recency predictions in this group can be minimized by elimination of patients with a CD4 count of less than 100 cells/mm3 or without no p31 antibodies. For 566 cross sectional sample the outcome of the decision tree confirmed the infection timing based on the results of all 3 markers but reduced the overall cost from 13.2 USD to 5.2 USD per sample. CONCLUSIONS: A step-wise multi assay decision tree allows accurate timing of the HIV infection at diagnosis at affordable effort and cost and can be an important new tool in studies analyzing the dynamics of local epidemics or the effects of prevention strategies.

Impact of Bacterial Infections on COVID-19 Patients: Is Timing Important?

BACKGROUND: Along with important factors that worsen the clinical outcome of COVID-19, it has been described that bacterial infections among patients positive for a SARS-CoV-2 infection can play a dramatic role in the disease process. Co-infections or community-acquired infections are recognized within the first 48 h after the admission of patients. Superinfections occur at least 48 h after admission and are considered to contribute to a worse prognosis. Microbiologic parameters differentiate infections that happen after the fifth day of hospitalization from those appearing earlier. Specifically, after the fifth day, the detection of resistant bacteria increases and difficult microorganisms emerge. OBJECTIVES: The aim of the study was to evaluate the impact of bacterial infections in patients with COVID-19 on the length of the hospital stay and mortality. METHODS: A total of 177 patients hospitalized due to COVID-19 pneumonia were consecutively sampled during the third and fourth wave of the pandemic at a University Hospital in Greece. A confirmed bacterial infection was defined as positive blood, urinary, bronchoalveolar lavage (BAL) or any other infected body fluid. Patients with confirmed infections were further divided into subgroups according to the time from admission to the positive culture result. RESULTS: When comparing the groups of patients, those with a confirmed infection had increased odds of death (odds ratio: 3.634; CI 95%: 1.795-7.358; p < 0.001) and a longer length of hospital stay (median 13 vs. 7 days). A late onset of infection was the most common finding in our cohort and was an independent risk factor for in-hospital death. Mortality and the length of hospital stay significantly differed between the subgroups. CONCLUSION: In this case series, microbial infections were an independent risk factor for a worse outcome among patients with COVID-19. Further, a correlation between the onset of infection and a negative outcome in terms of non-infected, community-acquired, early hospital-acquired and late hospital-acquired infections was identified. Late hospital-acquired infections increased the mortality of COVID-19 patients whilst superinfections were responsible for an extended length of hospital stay.

Timing HIV infection with a simple and accurate population viral dynamics model.

Clinical trials for HIV prevention can require knowledge of infection times to subsequently determine protective drug levels. Yet, infection timing is difficult when study visits are sparse. Using population nonlinear mixed-effects (pNLME) statistical inference and viral loads from 46 RV217 study participants, we developed a relatively simple HIV primary infection model that achieved an excellent fit to all data. We also discovered that Aptima assay values from the study strongly correlated with viral loads, enabling imputation of very early viral loads for 28/46 participants. Estimated times between infecting exposures and first positives were generally longer than prior estimates (average of two weeks) and were robust to missing viral upslope data. On simulated data, we found that tighter sampling before diagnosis improved estimation more than tighter sampling after diagnosis. Sampling weekly before and monthly after diagnosis was a pragmatic design for good timing accuracy. Our pNLME timing approach is widely applicable to other infections with existing mathematical models. The present model could be used to simulate future HIV trials and may help estimate protective thresholds from the recently completed antibody-mediated prevention trials.