Allogeneic stem cell transplantation compared to conservative management in adults with inborn errors of immunity.

Allogeneic hematopoietic stem cell transplantation (alloSCT) is curative for severe inborn errors of immunity (IEIs), with recent data suggesting alloSCT in adulthood is safe and effective in selected patients. However, questions remain regarding the indications for and optimal timing of transplant. We retrospectively compared outcomes of transplanted vs matched nontransplanted adults with severe IEIs. Seventy-nine patients (aged ≥ 15 years) underwent alloSCT between 2008 and 2018 for IEIs such as chronic granulomatous disease (n = 20) and various combined immune deficiencies (n = 59). A cohort of nontransplanted patients from the French Centre de Référence Déficits Immunitaires Héréditaires registry was identified blindly for case-control analysis, with ≤3 matched controls per index patient, without replacement. The nontransplanted patients were matched for birth decade, age at last review greater than index patient age at alloSCT, chronic granulomatous disease or combined immune deficiencies, and autoimmune/lymphoproliferative complications. A total of 281 patients were included (79 transplanted, 202 nontransplanted). Median age at transplant was 21 years. Transplant indications were mainly lymphoproliferative disease (n = 23) or colitis (n = 15). Median follow-up was 4.8 years (interquartile range, 2.5-7.2). One-year transplant-related mortality rate was 13%. Estimated disease-free survival at 5 years was higher in transplanted patients (58% vs 33%; P = .007). Nontransplanted patients had an ongoing risk of severe events, with an increased mean cumulative number of recurrent events compared with transplanted patients. Sensitivity analyses removing patients with common variable immune deficiency and their matched transplanted patients confirm these results. AlloSCT prevents progressive morbidity associated with IEIs in adults, which may outweigh the negative impact of transplant-related mortality.

The risk of hospitalization for psychotic disorders following hospitalization for COVID-19: a French nationwide longitudinal study.

COVID-19, like other infectious diseases, may be a risk factor for psychotic disorders. We aimed to compare the proportions of hospitalizations for psychotic disorders in the 12 months following discharge from hospital for either COVID-19 or for another reason in the adult general population in France during the first wave of the pandemic. We conducted a retrospective longitudinal nationwide study using the national French administrative healthcare database. Psychotic disorders were first studied as a whole, and then chronic and acute disorders separately. The role of several adjustment factors, including sociodemographics, a history of psychotic disorder, the duration of the initial hospitalization, and the level of care received during that hospitalization, were also analyzed. Between 1 January 2020 and 30 June 2020, a total of 14,622 patients were hospitalized for psychotic disorders in the 12 months following discharge from hospital for either COVID-19 or another reason. Initial hospitalization for COVID-19 (vs. another reason) was associated with a lower rate of subsequent hospitalization for psychotic disorders (0.31% vs. 0.51%, odds ratio (OR) = 0.60, 95% confidence interval (CI) [0.53-0.67]). This was true for both chronic and acute disorders, even after adjusting for the various study variables. Importantly, a history of psychotic disorder was a major determinant of hospitalization for psychotic disorders (adjusted OR = 126.56, 95% CI [121.85-131.46]). Our results suggest that, in comparison to individuals initially hospitalized for another reason, individuals initially hospitalized for COVID-19 present a lower risk of hospitalization for first episodes of psychotic symptoms/disorders or for psychotic relapse in the 12 months following discharge. This finding contradicts the hypothesis that there is a higher risk of psychotic disorders after a severe COVID-19.

Assessing model prediction performance for the expected cumulative number of recurrent events.

In a recurrent event setting, we introduce a new score designed to evaluate the prediction ability, for a given model, of the expected cumulative number of recurrent events. This score can be seen as an extension of the Brier Score for single time to event data but works for recurrent events with or without a terminal event. Theoretical results are provided that show that under standard assumptions in a recurrent event context, our score can be asymptotically decomposed as the sum of the theoretical mean squared error between the model and the true expected cumulative number of recurrent events and an inseparability term that does not depend on the model. This decomposition is further illustrated on simulations studies. It is also shown that this score should be used in comparison with a reference model, such as a nonparametric estimator that does not include the covariates. Finally, the score is applied for the prediction of hospitalisations on a dataset of patients suffering from atrial fibrillation and a comparison of the prediction performances of different models, such as the Cox model, the Aalen Model or the Ghosh and Lin model, is investigated.

Fast approximations of pseudo-observations in the context of right censoring and interval censoring.

In the context of right-censored and interval-censored data, we develop asymptotic formulas to compute pseudo-observations for the survival function and the restricted mean survival time (RMST). These formulas are based on the original estimators and do not involve computation of the jackknife estimators. For right-censored data, Von Mises expansions of the Kaplan-Meier estimator are used to derive the pseudo-observations. For interval-censored data, a general class of parametric models for the survival function is studied. An asymptotic representation of the pseudo-observations is derived involving the Hessian matrix and the score vector. Theoretical results that justify the use of pseudo-observations in regression are also derived. The formula is illustrated on the piecewise-constant-hazard model for the RMST. The proposed approximations are extremely accurate, even for small sample sizes, as illustrated by Monte Carlo simulations and real data. We also study the gain in terms of computation time, as compared to the original jackknife method, which can be substantial for a large dataset.

Is Placental Malaria a Long-term Risk Factor for Mild Malaria Attack in Infancy? Revisiting a Paradigm.

Background: Children born to mothers with placental malaria (PM) have been described as more susceptible to the occurrence of a first malaria infection. However, whether or not these children remain more at risk during infancy has never been explored. We aimed to determine if children born to mothers with PM are more susceptible to malaria and remain at higher risk between birth and 18 months. Methods: Five hundred fifty children were followed up weekly with control of temperature and, if >37.5°C, both a rapid diagnostic test for malaria and a thick blood smear were performed. Taking into account environmental risk of infection, the relationship between occurrences of malaria attacks from birth to 18 months was modeled using Cox models for recurrent events. Results: PM is not associated with an overall susceptibility to malaria but only with the delay of occurrence of the first malaria attack. Children born from mothers with PM tend to have an increased risk for the first malaria attack (hazard ratio [HR] = 1.33; P = .048) but not for subsequent ones (HR = 0.9; P = .46). Children who experienced 1 malaria attack were strongly at risk to develop subsequent infections independent of placental infection and environmental exposure. Conclusions: These results are consistent with the existence of an individual susceptibility to malaria unrelated to PM. From a public health point of view, protecting children born to infected placenta remains a priority, but seems insufficient to account for other frail children for whom a biomarker of frailty needs to be found.

Lower risk of hospitalisation for depression following hospitalisation for COVID-19 versus for another reason.

INTRODUCTION: Although hospitalisation for COVID-19 is associated with a higher post-discharge risk of mood disorders, including major depressive disorder (MDD) and bipolar disorder (BD), this risk has not been compared to that following hospitalisation for a reason other than COVID-19. METHODS: Using data from France's National Health Data System (SNDS) database, we compared patients hospitalised for mood disorders in the 12 months following COVID-19/another reason hospitalisation. RESULTS: 96,313 adult individuals were hospitalised for COVID-19, and 2,979,775 were hospitalised for another reason. In the 12 months post-discharge, 110,976 (3.83 %) patients were hospitalised for mood disorders. In unadjusted analyses, patients initially hospitalised for COVID-19 (versus another reason) were more likely to be subsequently hospitalised for a mood disorder (4.27 % versus 3.82 % versus, respectively, p < 0.0001). These patients were also more likely to have a history of mood disorders, especially depressive disorders (6.45 % versus 5.77 %, respectively, p < 0.0001). Women, older age, lower social deprivation, a history of mood disorders, longer initial hospitalisation (COVID-19 or other), and a higher level of clinical care during initial hospitalisation were all significantly associated with the risk of subsequent hospitalisation for MDD and BD. In contrast, after adjusting for all these factors, persons initially hospitalised for COVID-19 were less likely to be subsequently hospitalised for MDD (OR = 0.902 [0.870-0.935]; p < 0.0001). No difference between both groups was observed for BD. LIMITATIONS: Other reasons were not separately studied. CONCLUSIONS: After adjusting for confounding factors, initial hospitalisation for COVID-19 versus for another reason was associated with a lower risk of hospitalisation for a mood disorder.

Can machine learning provide preoperative predictions of biological hemostasis after extracorporeal circulation for cardiac surgery?

OBJECTIVES: The goal of this study was to improve decision making regarding the transfusion of patients at the end of extracorporeal circulation for cardiac surgery through machine learning predictions of the evolution of platelets counts, prothrombin ratio, and fibrinogen assay. METHODS: Prospective data with information about patient preoperative biology and surgery characteristics were collected at Institut Mutualiste Montsouris Hospital (Paris, France) for 10 months (n = 598). For each outcome of interest, instead of arbitrarily choosing 1 machine learning algorithm, we trained and tested a variety of algorithms together with the super learning algorithm, a state-of-the-art ensemble method that aggregates all the predictions and selects the best performing algorithm (total, 137 algorithms). We considered the top-performing algorithms and compared them to more standard and interpretable multivariable linear regression models. All algorithms were evaluated through their root mean squared error, a measure of the average difference between true and predicted values. RESULTS: The root mean squared error of the top algorithms for predicting the difference between pre- and postoperative platelet counts, prothrombin ratio, and fibrinogen assay were 38.27 × 10e9/L, 8.66%, and 0.44 g/L, respectively. The linear models had similar performances. CONCLUSIONS: Our machine learning algorithms accurately predicted prothrombin ratio and fibrinogen assay and less accurately platelet counts. As such, our models could provide an aid-decision tool for anesthetists in an operating room; future clinical trials addressing this hypothesis are warranted.

A Polymer-Based Metallurgical Route to Produce Aluminum Metal-Matrix Composite with High Strength and Ductility.

In this investigation, an attempt was made to develop a new high-strength and high-ductility aluminum metal-matrix composite. It was achieved by incorporating ceramic reinforcement into the metal which was formed in situ from a polymer by pyrolysis. A crosslinked PMHS polymer was introduced into commercially pure aluminum via friction stir processing (FSP). The distributed micro- and nano-sized polymer was then converted into ceramic particles by heating at 500 °C for 10 h and processed again via FSP. The produced composite showed a 2.5-fold increase in yield strength (to 119 MPa from 48 MPa) and 3.5-fold increase in tensile strength (to 286 MPa from 82 MPa) with respect to the base metal. The ductility was marginally reduced from 40% to 30%. The increase in strength is attributed to the grain refinement and the larger ceramic particles. High-temperature grain stability was obtained, with minimal loss to mechanical properties, up to 500 °C due to the Zenner pinning effect of the nano-sized ceramic particles at the grain boundaries. Fractures took place throughout the matrix up to 300 °C. Above 300 °C, the interfacial bonding between the particle and matrix became weak, and fractures took place at the particle-matrix interface.

Regression modelling of interval censored data based on the adaptive ridge procedure.

A new method for the analysis of time to ankylosis complication on a dataset of replanted teeth is proposed. In this context of left-censored, interval-censored and right-censored data, a Cox model with piecewise constant baseline hazard is introduced. Estimation is carried out with the expectation maximisation (EM) algorithm by treating the true event times as unobserved variables. This estimation procedure is shown to produce a block diagonal Hessian matrix of the baseline parameters. Taking advantage of this interesting feature in the EM algorithm, a L 0 penalised likelihood method is implemented in order to automatically determine the number and locations of the cuts of the baseline hazard. This procedure allows to detect specific areas of time where patients are at greater risks for ankylosis. The method can be directly extended to the inclusion of exact observations and to a cure fraction. Theoretical results are obtained which allow to derive statistical inference of the model parameters from asymptotic likelihood theory. Through simulation studies, the penalisation technique is shown to provide a good fit of the baseline hazard and precise estimations of the resulting regression parameters.

Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases.

Micro-RNAs (miRNAs) are short (∼21 nt) non-coding RNAs that regulate gene expression through the degradation or translational repression of mRNAs. Accumulating evidence points to a role of miRNA regulation in the pathogenesis of a wide range of neurodegenerative (ND) diseases such as, for example, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and Huntington disease (HD). Several systems level studies aimed to explore the role of miRNA regulation in NDs, but these studies remain challenging. Part of the problem may be related to the lack of sufficiently rich or homogeneous data, such as time series or cell-type-specific data obtained in model systems or human biosamples, to account for context dependency. Part of the problem may also be related to the methodological challenges associated with the accurate system-level modeling of miRNA and mRNA data. Here, we critically review the main families of machine learning methods used to analyze expression data, highlighting the added value of using shape-analysis concepts as a solution for precisely modeling highly dimensional miRNA and mRNA data such as the ones obtained in the study of the HD process, and elaborating on the potential of these concepts and methods for modeling complex omics data.

Hospitalization for COVID-19 is associated with a higher risk of subsequent hospitalization for psychiatric disorders: A French nationwide longitudinal study comparing hospitalizations for COVID-19 and for other reasons.

INTRODUCTION: Although COVID-19 has been associated with psychiatric symptoms in patients, no study to date has examined the risk of hospitalization for psychiatric disorders after hospitalization for this disease. OBJECTIVE: We aimed to compare the proportions of hospitalizations for psychiatric disorders in the 12 months following either hospitalization for COVID-19 or hospitalization for another reason in the adult general population in France during the first wave of the current pandemic. METHODS: We conducted a retrospective longitudinal nationwide study based on the national French administrative healthcare database. RESULTS: Among the 2,894,088 adults hospitalized, 96,313 (3.32%) were admitted for COVID-19. The proportion of patients subsequently hospitalized for a psychiatric disorder was higher for COVID-19 patients (11.09 vs. 9.24%, OR = 1.20 95%CI 1.18-1.23). Multivariable analyses provided similar results for a psychiatric disorder of any type and for psychotic and anxiety disorders (respectively, aOR = 1.06 95%CI 1.04-1.09, aOR = 1.09 95%CI 1.02-1.17, and aOR = 1.11 95%CI 1.08-1.14). Initial hospitalization for COVID-19 in intensive care units and psychiatric history were associated with a greater risk of subsequent hospitalization for any psychiatric disorder than initial hospitalization for another reason. DISCUSSION: Compared with hospitalizations for other reasons, hospitalizations for COVID-19 during the first wave of the pandemic in France were associated with a higher risk of hospitalization for a psychiatric disorder during the 12 months following initial discharge. This finding should encourage clinicians to increase the monitoring and assessment of psychiatric symptoms after hospital discharge for COVID-19, and to propose post-hospital care, especially for those treated in intensive care.

Influence of exposure assessment methods on associations between long-term exposure to outdoor fine particulate matter and risk of cancer in the French cohort Gazel.

BACKGROUND: Many studies investigated the relationship between outdoor fine particulate matter (PM2.5) and cancer. While they generally indicated positive associations, results have not been fully consistent, possibly because of the diversity of methods used to assess exposure. OBJECTIVES: To investigate how using different PM2.5 exposure assessment methods influences risk estimates in the large French general population-based Gazel cohort (20,625 participants at enrollment) with a 26-year follow-up with complete residential histories. METHODS: We focused on two cancer incidence outcomes: all-sites combined and lung. We used two distinct exposure assessment methods: a western European land use regression (LUR), and a chemistry-dispersion model (Gazel-Air) for France, each with a time series ≥20-years annual concentrations. Spearman correlation coefficient between the two estimates of PM2.5 was 0.71 across all person-years; the LUR tended to provide higher exposures. We used extended Cox models with attained age as time-scale and time-dependent cumulative exposures, adjusting for a set of confounders including sex and smoking, to derive hazard ratios (HRs) and their 95% confidence interval, implementing a 10-year lag between exposure and incidence/censoring. RESULTS: We obtained similar two-piece linear associations for all-sites cancer (3711 cases), with a first slope of HRs of 1.53 (1.24-1.88) and 1.43 (1.19-1.73) for one IQR increase of cumulative PM2.5 exposure for the LUR and the Gazel-Air models respectively, followed by a plateau at around 1.5 for both exposure assessments. For lung cancer (349 cases), the HRs from the two exposure models were less similar, with largely overlapping confidence limits. CONCLUSION: Our findings using long-term exposure estimates from two distinct exposure assessment methods corroborate the association between air pollution and cancer risk.

Regularized bidimensional estimation of the hazard rate.

In epidemiological or demographic studies, with variable age at onset, a typical quantity of interest is the incidence of a disease (for example the cancer incidence). In these studies, the individuals are usually highly heterogeneous in terms of dates of birth (the cohort) and with respect to the calendar time (the period) and appropriate estimation methods are needed. In this article a new estimation method is presented which extends classical age-period-cohort analysis by allowing interactions between age, period and cohort effects. We introduce a bidimensional regularized estimate of the hazard rate where a penalty is introduced on the likelihood of the model. This penalty can be designed either to smooth the hazard rate or to enforce consecutive values of the hazard to be equal, leading to a parsimonious representation of the hazard rate. In the latter case, we make use of an iterative penalized likelihood scheme to approximate the L0 norm, which makes the computation tractable. The method is evaluated on simulated data and applied on breast cancer survival data from the SEER program.

Increasing Necking Strain through Corrugation: Identifying Composite Systems That Can Benefit from Corrugated Geometry.

Under some circumstances, composites with a corrugated reinforcement geometry show larger necking strains compared to traditional straight reinforced composites. In this work, finite element modeling studies were performed for linearly hardening materials, examining the effect of material parameters on the stress-strain response of both corrugation and straight-reinforced composites. These studies showed that improvements in necking strain depend on the ability of the corrugation to unbend and to provide a boost in work hardening at the right time. It was found that there is a range of matrix yield strengths and hardening rates for which a corrugated geometry will improve the necking strain and also a lower threshold of reinforcement yield strength below which no improvement in necking strain is possible. In addition, benefit maps and surfaces were generated that show which regions of property space benefit through corrugation and the corresponding improvement in necking strain that can be achieved.

Recurrent event survival analysis predicts future risk of hospitalization in patients with paroxysmal and persistent atrial fibrillation.

BACKGROUND: In patients with paroxysmal atrial fibrillation (PAF) or persistent atrial fibrillation (PeAF) symptom burden and fear of hospital readmission are major causes of reduced quality of life. We attempted to develop a prediction model for future atrial fibrillation hospitalization (AFH) risk in PAF and PeAF patients including all previously experienced AFHs in the analysis, as opposed to time to first event. METHODS: Recurrent event survival analysis was used to model the impact of past AFHs on the risk of future AFHs. A recurrent event was defined as a hospitalization due to a new episode of AF. Death or progression to permanent AF were included as competing risks. RESULTS: We enrolled 174 patients with PAF or PeAF, mean follow up duration was 1279 days, and 325 AFHs were observed. Median patient age was 63.0 (IQR 52.2-68.0), 29% had PAF, and 71% were male. Highly significant predictors of future AFH risk were PeAF (HR 3.20, CI 2.01-5.11) and number of past AFHs observed (HR for 1 event: 2.97, CI 2.04-4.32, HR for ≥2 events: 7.54, CI 5.47-10.40). CONCLUSION: In PAF and PeAF patients, AF type and observed AFH frequency are highly significant predictors of future AFH risk. The developed model enables risk prediction in individual patients based on AFH history and baseline characteristics, utilizing all events experienced by the patient. This is the first time recurrent event survival analysis has been used in AF patients.

A change-point model for detecting heterogeneity in ordered survival responses.

In this article, we suggest a new statistical approach considering survival heterogeneity as a breakpoint model in an ordered sequence of time-to-event variables. The survival responses need to be ordered according to a numerical covariate. Our estimation method will aim at detecting heterogeneity that could arise through the ordering covariate. We formally introduce our model as a constrained Hidden Markov Model, where the hidden states are the unknown segmentation (breakpoint locations) and the observed states are the survival responses. We derive an efficient Expectation-Maximization framework for maximizing the likelihood of this model for a wide range of baseline hazard forms (parametrics or nonparametric). The posterior distribution of the breakpoints is also derived, and the selection of the number of segments using penalized likelihood criterion is discussed. The performance of our survival breakpoint model is finally illustrated on a diabetes dataset where the observed survival times are ordered according to the calendar time of disease onset.

Computing Individual Risks Based on Family History in Genetic Disease in the Presence of Competing Risks.

When considering a genetic disease with variable age at onset (e.g., familial amyloid neuropathy, cancers), computing the individual risk of the disease based on family history (FH) is of critical interest for both clinicians and patients. Such a risk is very challenging to compute because (1) the genotype X of the individual of interest is in general unknown, (2) the posterior distribution ℙ(X∣FH, T > t) changes with t (T is the age at disease onset for the targeted individual), and (3) the competing risk of death is not negligible. In this work, we present modeling of this problem using a Bayesian network mixed with (right-censored) survival outcomes where hazard rates only depend on the genotype of each individual. We explain how belief propagation can be used to obtain posterior distribution of genotypes given the FH and how to obtain a time-dependent posterior hazard rate for any individual in the pedigree. Finally, we use this posterior hazard rate to compute individual risk, with or without the competing risk of death. Our method is illustrated using the Claus-Easton model for breast cancer. The competing risk of death is derived from the national French registry.

Broad-Spectrum Antibiotic Treatment and Subsequent Childhood Type 1 Diabetes: A Nationwide Danish Cohort Study.

BACKGROUND: Studies link antibiotic treatment and delivery by cesarean section with increased risk of chronic diseases through changes of the gut-microbiota. We aimed to evaluate the association of broad-spectrum antibiotic treatment during the first two years of life with subsequent onset of childhood type 1 diabetes and the potential effect-modification by mode of delivery. MATERIALS AND METHODS: A Danish nationwide cohort study including all singletons born during 1997-2010. End of follow-up by December 2012. Four national registers provided information on antibiotic redemptions, outcome and confounders. Redemptions of antibiotic prescriptions during the first two years of life was classified into narrow-spectrum or broad-spectrum antibiotics. Children were followed from age two to fourteen, both inclusive. The risk of type 1 diabetes with onset before the age of 15 years was assessed by Cox regression. A total of 858,201 singletons contributed 5,906,069 person-years, during which 1,503 children developed type 1 diabetes. RESULTS: Redemption of broad-spectrum antibiotics during the first two years of life was associated with an increased rate of type 1 diabetes during the following 13 years of life (HR 1.13; 95% CI 1.02 to 1.25), however, the rate was modified by mode of delivery. Broad-spectrum antibiotics were associated with an increased rate of type 1 diabetes in children delivered by either intrapartum cesarean section (HR 1.70; 95% CI 1.15 to 2.51) or prelabor cesarean section (HR 1.63; 95% CI 1.11 to 2.39), but not in vaginally delivered children. Number needed to harm was 433 and 562, respectively. The association with broad-spectrum antibiotics was not modified by parity, genetic predisposition or maternal redemption of antibiotics during pregnancy or lactation. CONCLUSIONS: Redemption of broad-spectrum antibiotics during infancy is associated with an increased risk of childhood type 1 diabetes in children delivered by cesarean section.