Multiple time scales in modeling the incidence of infections acquired in intensive care units

A safety study evaluating non-COVID-19 mortality risk following COVID-19 vaccination

BACKGROUND

The safety of COVID-19 vaccines plays an important role in addressing vaccine hesitancy. We conducted a large cohort study to evaluate the risk of non-COVID-19 mortality after COVID-19 vaccination while adjusting for confounders including individual-level demographics, clinical risk factors, health care utilization, and community-level socioeconomic risk factors.

METHODS

The retrospective cohort study consisted of members from seven Vaccine Safety Datalink sites from December 14, 2020 through August 31, 2021. We conducted three separate analyses for each of the three COVID-19 vaccines used in the US. Crude non-COVID-19 mortality rates were reported by vaccine type, age, sex, and race/ethnicity. The counting process model for survival analyses was used to analyze non-COVID-19 mortality where a new observation period began when the vaccination status changed upon receipt of the first dose and the second dose. We used calendar time as the basic time scale in survival analyses to implicitly adjust for season and other temporal trend factors. A propensity score approach was used to adjust for the potential imbalance in confounders between the vaccinated and comparison groups.

RESULTS

For each vaccine type and across age, sex, and race/ethnicity groups, crude non-COVID-19 mortality rates among COVID-19 vaccinees were lower than those among comparators. After adjusting for confounders with the propensity score approach, the adjusted hazard ratios (aHRs) were 0.46 (95% confidence interval [CI], 0.44-0.49) after dose 1 and 0.48 (95% CI, 0.46-0.50) after dose 2 of the BNT162b2 vaccine, 0.41 (95% CI, 0.39-0.44) after dose 1 and 0.38 (95% CI, 0.37-0.40) after dose 2 of the mRNA-1273 vaccine, and 0.55 (95% CI, 0.51-0.59) after receipt of Ad26.COV2.S.

CONCLUSION

While residual confounding bias remained after adjusting for several individual-level and community-level risk factors, no increased risk was found for non-COVID-19 mortality among recipients of three COVID-19 vaccines used in the US.

Vaccine Effectiveness Against SARS-CoV-2 Infection and Severe Outcomes in the Maintenance Dialysis Population in Ontario, Canada

BACKGROUND

Vaccination studies in the hemodialysis population have demonstrated decreased antibody response compared with healthy controls, but vaccine effectiveness for preventing SARS-CoV-2 infection and severe disease is undetermined.

METHODS

We conducted a retrospective cohort study in the province of Ontario, Canada, between December 21, 2020, and June 30, 2021. Receipt of vaccine, SARS-CoV-2 infection, and related severe outcomes (hospitalization or death) were determined from provincial health administrative data. Receipt of one and two doses of vaccine were modeled in a time-varying cause-specific Cox proportional hazards model, adjusting for baseline characteristics, background community infection rates, and censoring for non-COVID death, recovered kidney function, transfer out of province, solid organ transplant, and withdrawal from dialysis.

RESULTS

Among 13,759 individuals receiving maintenance dialysis, 2403 (17%) were unvaccinated and 11,356 (83%) had received at least one dose by June 30, 2021. Vaccine types were BNT162b2 (n=8455, 74%) and mRNA-1273 (n=2901, 26%); median time between the first and second dose was 36 days (IQR 28-51). The adjusted hazard ratio (HR) for SARS-CoV-2 infection and severe outcomes for one dose compared with unvaccinated was 0.59 (95% CI, 0.46 to 0.76) and 0.54 (95% CI, 0.37 to 0.77), respectively, and for two doses compared with unvaccinated was 0.31 (95% CI, 0.22 to 0.42) and 0.17 (95% CI, 0.1 to 0.3), respectively. There were no significant differences in vaccine effectiveness among age groups, dialysis modality, or vaccine type.

CONCLUSIONS

COVID-19 vaccination is effective in the dialysis population to prevent SARS-CoV-2 infection and severe outcomes, despite concerns about suboptimal antibody responses.

Assessment of 28-Day In-Hospital Mortality in Mechanically Ventilated Patients With Coronavirus Disease 2019: An International Cohort Study

Supplemental Digital Content is available in the text.

Factors associated with mortality in coronavirus disease 2019 patients on invasive mechanical ventilation are still not fully elucidated.

A multistate model incorporating estimation of excess hazards and multiple time scales

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population-level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user-friendly Stata software with accompanying example code.

Exploring the usefulness of Lexis diagrams for quality improvement

BACKGROUND

Visualization is important to aid practitioners in understanding local care processes and drive quality improvement (QI). Important aspects include timely feedback and ability to plot data over time. Moreover, the complexity of care also needs to be understood, as it affects the variation of care processes. However, there is a lack of QI methods visualizing multiple, related factors such as diagnosis date, death date, and cause of death to unravel their complexity, which is necessary to understand processes related to survival data. Lexis diagrams visualize individual patient processes as lines and mark additional factors such as key events. This study explores the potential of Lexis diagrams to support QI through survival data analysis, focusing on feedback, timeliness, and complexity, in a gynecological cancer setting in Sweden.

METHODS

Lexis diagrams were produced based on data from a gynecological cancer quality registry (4481 patients). The usefulness of Lexis diagrams was explored through iterative data identification and analysis through semi-structured dialogues between the researcher and domain experts (clinically active care process owners) during five meetings. Visualizations were produced and adapted by the researcher between meetings, based on the dialogues, to ensure clinical relevance, resulting in three relevant types of visualizations.

RESULTS

Domain experts identified different uses depending on diagnosis group and data visualization. Key results include timely feedback through close-to-real-time visualizations, supporting discussion and understanding of trends and hypothesis-building. Visualization of care process complexity facilitated evaluation of given care. Combined visualization of individual and population levels increased patient focus and may possibly also function to motivate practitioners and management.

CONCLUSION

Lexis diagrams can aid understanding of survival data, triggering important dialogues between care givers and supporting care quality improvement and new perspectives, and can therefore complement survival curves in quality improvement.

Septic patients in the intensive care unit present different nasal microbiotas

AIM

The primary objective of this study was to evaluate correlations among mortality, intensive care unit (ICU) length of stay and airway microbiotas in septic patients.

MATERIALS & METHODS

A deep-sequencing analysis of the 16S rRNA gene V4 region was performed.

RESULTS

The nasal microbiota in septic patients was dominated by three nasal bacterial types (Corynebacterium, Staphylococcus and Acinetobacter). The Acinetobacter type was associated with the lowest diversity and longest length of stay (median: 9 days), and the Corynebacterium type was associated with the shortest length of stay. We found that the Acinetobacter type in the >9-day group was associated with the highest mortality (33%).

CONCLUSION

Septic patients have three nasal microbiota types, and the nasal microbiota is related to the length of stay and mortality.