Better COVID-19 Intensive Care Unit survival in females, independent of age, disease severity, comorbidities, and treatment

Although male Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) patients have higher Intensive Care Unit (ICU) admission rates and a worse disease course, a comprehensive analysis of female and male ICU survival and underlying factors such as comorbidities, risk factors, and/or anti-infection/inflammatory therapy administration is currently lacking. Therefore, we investigated the association between sex and ICU survival, adjusting for these and other variables. In this multicenter observational cohort study, all patients with SARS-CoV-2 pneumonia admitted to seven ICUs in one region across Belgium, The Netherlands, and Germany, and requiring vital organ support during the first pandemic wave were included. With a random intercept for a center, mixed-effects logistic regression was used to investigate the association between sex and ICU survival. Models were adjusted for age, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, comorbidities, and anti-infection/inflammatory therapy. Interaction terms were added to investigate effect modifications by sex with country and sex with obesity. A total of 551 patients (29% were females) were included. Mean age was 65.4 ± 11.2 years. Females were more often obese and smoked less frequently than males (p-value 0.001 and 0.042, respectively). APACHE II scores of females and males were comparable. Overall, ICU mortality was 12% lower in females than males (27% vs 39% respectively, p-value < 0.01) with an odds ratio (OR) of 0.62 (95%CI 0.39–0.96, p-value 0.032) after adjustment for age and APACHE II score, 0.63 (95%CI 0.40–0.99, p-value 0.044) after additional adjustment for comorbidities, and 0.63 (95%CI 0.39–0.99, p-value 0.047) after adjustment for anti-infection/inflammatory therapy. No effect modifications by sex with country and sex with obesity were found (p-values for interaction > 0.23 and 0.84, respectively). ICU survival in female SARS-CoV-2 patients was higher than in male patients, independent of age, disease severity, smoking, obesity, comorbidities, anti-infection/inflammatory therapy, and country. Sex-specific biological mechanisms may play a role, emphasizing the need to address diversity, such as more sex-specific prediction, prognostic, and therapeutic approach strategies.

Glucose-Lowering Medications and Post-Dementia Survival in Patients with Diabetes and Dementia

Background: The effectiveness of glucose-lowering drugs (GLDs) is unknown among patients with dementia. Objective: To analyze all-cause mortality among users of six GLDs in dementia and dementia-free subjects, respectively. Methods: This was a longitudinal open-cohort registry-based study using data from the Swedish Dementia Registry, Total Population Register, and four supplemental registers providing data on dementia status, drug usage, confounders, and mortality. The cohort comprised 132,402 subjects with diabetes at baseline, of which 11,401 (8.6%) had dementia and 121,001 (91.4%) were dementia-free. Subsequently, comparable dementia – dementia-free pairs were sampled. Then, as-treated and intention-to-treat exposures to metformin, insulin, sulfonylurea, dipeptidyl-peptidase-4 inhibitors, glucagon-like peptide-1 analogues (GLP-1a), and sodium-glucose cotransporter-2 inhibitors (SGLT-2i) were analyzed in the parallel dementia and dementia-free cohorts. Confounding was addressed using inverse-probability weighting and propensity-score matching, and flexible parametric survival models were used to produce hazard ratios (HR) and 95% confidence intervals (CI) of the association between GLDs and all-cause mortality. Results: In the as-treated models, increased mortality was observed among insulin users with dementia (HR 1.34 [95%CI 1.24–1.45]) as well as in dementia-free subjects (1.54 [1.10–1.55]). Conversely, sulfonylurea was associated with higher mortality only in dementia subjects (1.19 [1.01–1.42]). GLP-1a (0.44 [0.25–0.78]) and SGLT-2i users with dementia (0.43 [0.23–0.80]) experienced lower mortality compared to non-users. Conclusion: Insulin and sulfonylurea carried higher mortality risk among dementia patients, while GLP-1a and SGLT-2i were associated with lower risk. GLD-associated mortality varied between dementia and comparable dementia-free subjects. Further studies are needed to optimize GLD use in dementia patients.

Is Police Misconduct Contagious? Non-trivial Null Findings from Dallas, Texas

Objectives Understanding if police malfeasance might be “contagious” is vital to identifying efficacious paths to police reform. Accordingly, we investigate whether an officer’s propensity to engage in misconduct is associated with her direct, routine interaction with colleagues who have themselves engaged in misbehavior in the past. Methods Recognizing the importance of analyzing the actual social networks spanning a police force, we use data on collaborative responses to 1,165,136 “911” calls for service by 3475 Dallas Police Department (DPD) officers across 2013 and 2014 to construct daily networks of front-line interaction. And we relate these cooperative networks to reported and formally sanctioned misconduct on the part of the DPD officers during the same time period using repeated-events survival models. Results Estimates indicate that the risk of a DPD officer engaging in misconduct is not associated with the disciplined misbehavior of her ad hoc, on-the-scene partners. Rather, a greater risk of misconduct is associated with past misbehavior, officer-specific proneness, the neighborhood context of patrol, and, in some cases, officer race, while departmental tenure is a mitigating factor. Conclusions Our observational findings—based on data from one large police department in the United States—ultimately suggest that actor-based and ecological explanations of police deviance should not be summarily dismissed in favor of accounts emphasizing negative socialization, where our study design also raises the possibility that results are partly driven by unobserved trait-based variation in the situations that officers find themselves in. All in all, interventions focused on individual officers, including the termination of deviant police, may be fruitful for curtailing police misconduct—where early interventions focused on new offenders may be key to avoiding the escalation of deviance.

dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

Objective Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but a manual analysis workflow hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. Results We introduce a package ( dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data.

Stacked survival models for residual lifetime data

When modelling the survival distribution of a disease for which the symptomatic progression of the associated condition is insidious, it is not always clear how to measure the failure/censoring times from some true date of disease onset. In a prevalent cohort study with follow-up, one approach for removing any potential influence from the uncertainty in the measurement of the true onset dates is through the utilization of only the residual lifetimes. As the residual lifetimes are measured from a well-defined screening date (prevalence day) to failure/censoring, these observed time durations are essentially error free. Using residual lifetime data, the nonparametric maximum likelihood estimator (NPMLE) may be used to estimate the underlying survival function. However, the resulting estimator can yield exceptionally wide confidence intervals. Alternatively, while parametric maximum likelihood estimation can yield narrower confidence intervals, it may not be robust to model misspecification. Using only right-censored residual lifetime data, we propose a stacking procedure to overcome the non-robustness of model misspecification; our proposed estimator comprises a linear combination of individual nonparametric/parametric survival function estimators, with optimal stacking weights obtained by minimizing a Brier Score loss function.

Metabolic syndrome predicts new damage in systemic lupus erythematosus patients: Data from the Almenara Lupus Cohort

Objectives This study aims to determine whether the MetS predicts damage accrual in SLE patients. Methods This longitudinal study was conducted in a cohort of consecutive SLE patients seen since 2012 at one single Peruvian institution. Patients had a baseline visit and then follow-up visits every 6 months. Patients with ≥ 2 visits were included. Evaluations included interview, medical records review, physical examination, and laboratory tests. Damage accrual was ascertained with the SLICC/ACR damage index (SDI) and disease activity with the SLEDAI-2K. Univariable and multivariable Cox-regression survival models were carried out to determine the risk of developing new damage. The multivariable model was adjusted for age at diagnosis; disease duration; socioeconomic status; SLEDAI; baseline SDI; the Charlson Comorbidity Index; daily dose; and time of exposure of prednisone (PDN), antimalarials, and immunosuppressive drugs. Results Two hundred and forty-nine patients were evaluated; 232 of them were women (93.2%). Their mean (SD) age at diagnosis was 35.8 (13.1) years; nearly all patients were Mestizo. Disease duration was 7.4 (6.6) years. The SLEDAI-2K was 5.2 (4.3) and the SDI, 0.9 (1.3). One hundred and eight patients (43.4%) had MetS at baseline. During follow-up, 116 (46.6%) patients accrued at least one new point in the SDI damage index. In multivariable analyses, the presence of MetS was a predictor of the development of new damage (HR: 1.54 (1.05–2.26); p < 0.029). Conclusions The presence of MetS predicts the development of new damage in SLE patients, despite other well-known risk factors for such occurrence.

dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but a manual analysis workflow hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. We introduce a package (dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data. A tutorial in bookdown format with code, diagnostics, plots and synthetic data is available here: https://neelsoumya.github.io/dsSurvivalbookdown/ All code is available from the following repositories: https://github.com/neelsoumya/dsSurvivalClient/ https://github.com/neelsoumya/dsSurvival/

Does diabetes affect paraneoplastic thrombocytosis in colorectal cancer?

Background A large variety of factors can affect colorectal cancer (CRC) survival, including type 2 diabetes mellitus (T2DM) and paraneoplastic thrombocytosis. Although several common factors play a role in their development and platelets are damaged in both diseases, the combined relationship of the three conditions was never investigated previously. Methods A prospective, real-life observational cohort study was conducted with the inclusion of 108 CRC patients and 166 voluntary non-CRC subjects. Plasma interleukin-6 and thrombopoietin levels were measured. Results Study participants were divided into cohorts based on the presence of T2DM. Platelet count (p < 0.0500) and interleukin-6 (p < 0.0100) level were significantly higher in the CRC groups. Thrombopoietin level was higher in the T2DM, CRC, and CRC + T2DM groups (p < 0.0500). Analysis of parameter changes over time and survival models revealed that neither platelet count, interleukin-6, nor thrombopoietin levels were affected by T2DM. Death of patients was associated with higher baseline platelet count (p = 0.0042) and interleukin-6 level (p < 0.0001). Conclusion Although the independent, disease-worsening effect of paraneoplastic thrombocytosis and T2DM is known, the coexistence of the two did not further impair the survival of CRC patients, suggesting that T2DM has no significant effect over paraneoplastic thrombocytosis.

An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

Background Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial’s follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. Methods We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. Results There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily included any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. Conclusions We present a straightforward regression-based method for computing the EVSI of extending an existing trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. Highlights Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we have developed new methods for computing the EVSI of extending a trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations. The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.