Background The clinical progress of patients hospitalized due to COVID-19 is often associated with severe pneumonia which may require intensive care, invasive ventilation, or extracorporeal membrane oxygenation (ECMO). The length of intensive care and the duration of these supportive therapies are clinically relevant outcomes. From the statistical perspective, these quantities are challenging to estimate due to episodes being time-dependent and potentially multiple, as well as being determined by the competing, terminal events of discharge alive and death. Methods We used multistate models to study COVID-19 patients’ time-dependent progress and provide a statistical framework to estimate hazard rates and transition probabilities. These estimates can then be used to quantify average sojourn times of clinically important states such as intensive care and invasive ventilation. We have made two real data sets of COVID-19 patients (n = 24* and n = 53**) and the corresponding statistical code publically available. Results The expected lengths of intensive care unit (ICU) stay at day 28 for the two cohorts were 15.05* and 19.62** days, while expected durations of mechanical ventilation were 7.97* and 9.85** days. Predicted mortality stood at 51%* and 15%**. Patients mechanically ventilated at the start of the example studies had a longer expected duration of ventilation (12.25*, 14.57** days) compared to patients non-ventilated (4.34*, 1.41** days) after 28 days. Furthermore, initially ventilated patients had a higher risk of death (54%* and 20%** vs. 48%* and 6%**) after 4 weeks. These results are further illustrated in stacked probability plots for the two groups from time zero, as well as for the entire cohort which depicts the predicted proportions of the patients in each state over follow-up. Conclusions The multistate approach gives important insights into the progress of COVID-19 patients in terms of ventilation duration, length of ICU stay, and mortality. In addition to avoiding frequent pitfalls in survival analysis, the methodology enables active cases to be analyzed by allowing for censoring. The stacked probability plots provide extensive information in a concise manner that can be easily conveyed to decision makers regarding healthcare capacities. Furthermore, clear comparisons can be made among different baseline characteristics.
By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing inhospital COVID-19 data.
Background: Aim of the pilot study was the histologic classification of the inflamed peri-implant soft tissue around ceramic implants (CI) in comparison with titanium implants (TI). Methods: Peri-implant tissue were retrieved from 15 patients (aged 34 to 88 years, seven males/eight females) with severe peri-implantitis (eight CI, seven TI). The peri-implant soft tissue samples were retrieved from the sites during scheduled removal of the implant and prepared for immunohistochemical analysis. Monoclonal antibodies (targeting CD3, CD20, CD138, and CD68) were used to identify T-and B-cells, plasma cells and macrophages. Quantitative assessment was performed by one histologically trained investigator. Linear mixed regression models were used. Results: A similar numerical distribution of the cell population was found in peri-implantitis around CI compared with TI. CD3 (TI, 17% to 85% versus CI, 20% to 70% of total cell number) and CD138 (TI, 1% to 73% versus CI, 12% to 69% of total cell number) were predominantly expressed. Notably, patient-individual differences of numerical cell distribution were detected. Co-localization of Band T-lymphocytes was observed. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The success rate of dental implants depends on primary and secondary stability. We investigate predictive factors for future risk stratification models. We retrospectively analyze 272 patients with a total of 582 implants. Implant stability is measured with resonance frequency analysis and evaluated based on the implant stability quotient (ISQ). A linear regression model with regression coefficients (reg. coeff.) and its 95% confidence interval (95% CI) is applied to assess predictive factors for implant stability. Implant diameter (reg. coeff.: 3.28; 95% CI: 1.89–4.66, p < 0.001), implant length (reg. coeff.: 0.67, 95% CI: 0.26–1.08, p < 0.001), and implant localization (maxillary vs. mandibular, reg. coeff.: −7.45, 95% CI: −8.70–(−6.20), p < 0.001) are significant prognostic factors for primary implant stability. An increase in ISQ between insertion and exposure is significantly correlated with healing time (reg. coeff.: 0.11, 95% CI: 0.04–0.19). Patients with maxillary implants have lower ISQ at insertion but show a higher increase in ISQ after insertion than patients with mandibular implants. We observe positive associations between primary implant stability and implant diameter, implant length, and localization (mandibular vs. maxillary). An increase in implant stability between insertion and exposure is significantly correlated with healing time and is higher for maxillary implants. These predictive factors should be further evaluated in prospective cohort studies to develop future preoperative risk-stratification models.
IMPORTANCE Carriage of Staphylococcus aureus is associated with S aureus infection. However, associations between S aureus carriage and the development of S aureus intensive care unit (ICU) pneumonia (SAIP) have not been quantified accurately, and interpretation of available data is hampered because of variations in definitions. OBJECTIVE To quantify associations of patient-related and contextual factors, including S aureus colonization status, with the occurrence of SAIP. DESIGN, SETTING, AND PARTICIPANTS This cohort study was conducted in ICUs of 30 hospitals in 11 European countries, geographically spread across 4 regions. Among patients with an anticipated length of stay 48 hours or longer who were undergoing mechanical ventilation at ICU admission, S aureus colonization was ascertained in the nose and lower respiratory tract. From this group, S aureus-colonized and noncolonized patients were enrolled into the study cohort in a 1:1 ratio. Data analysis was performed from May to November 2019. MAIN OUTCOMES AND MEASURES SAIP was defined as any pneumonia during the ICU stay developing 48 hours or more after ICU admission with S aureus isolated from lower respiratory tract specimens or blood samples. The incidence of SAIP was derived in the study cohort and estimated on the weighted incidence calculation for the originating overarching population, while taking competing events into account. Weighted risk factor analysis was performed using Cox multivariable regression. RESULTS The study cohort consisted of 1933 patients (mean [SD] age, 62.0 [16.0] years); 1252 patients (64.8%) were men, and 950 patients (49.1%) were S aureus carriers at ICU admission. In all, 304 patients (15.7%) developed ICU-acquired pneumonia, of whom 131 patients (6.8%) had SAIP. Weighted SAIP incidences were 11.7 events per 1000 patient-days in the ICU for S aureus-colonized patients and 2.9 events per 1000 patient-days in the ICU for noncolonized patients (overall incidence, 4.9 events per 1000 patient-days in the ICU). The only factor independently associated with SAIP was S aureus colonization status at ICU admission (cause-specific hazard ratio, 3.6; 95% CI, 2.2-6.0; P < .001). There were marked regional differences in SAIP incidence and cause-specific hazard ratios for colonization status. CONCLUSIONS AND RELEVANCE SAIP incidence was 4.9 events per 1000 ICU patient-days for patients undergoing mechanical ventilation at ICU admission (or shortly thereafter). The daily risk of SAIP was 3.6 times higher in patients colonized with S aureus at ICU admission compared with noncolonized patients.
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