scholarly journals Reply to: Platelets level variability during the first year after liver transplantation in the risk prediction model for recipients mortality

2021 ◽  
Vol 23 ◽  
pp. 100323
Author(s):  
Michał Ciszek
2020 ◽  
Vol 19 (4) ◽  
pp. 417-421 ◽  
Author(s):  
Wojciech Jarmulski ◽  
Alicja Wieczorkowska ◽  
Mariusz Trzaska ◽  
Ewa Hryniewiecka ◽  
Leszek Pączek ◽  
...  

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3359-3359
Author(s):  
Emma Jacobine Verwaaijen ◽  
Jinhui Ma ◽  
Hester A. De Groot-Kruseman ◽  
Rob Pieters ◽  
Inge M. Van Der Sluis ◽  
...  

Abstract Introduction Due to bone fragility, children with acute lymphoblastic leukemia (ALL) have a 6-fold greater fracture risk during therapy compared to peers. Osteoporotic fractures are a concern, as they lead to adverse health outcomes including pain, loss of height due to vertebral deformity, and (transient) disability. In previous studies, lower lumbar spine bone mineral density (LS BMD) at ALL diagnosis was found to be prognostic for the occurrence of future fractures. However, routinely performing dual-energy X-ray absorptiometry (DXA) in each newly diagnosed child is not universally feasible. The aim of this study is to develop and validate an easy to use clinical risk prediction model for low lumbar spine bone mineral density (LS BMD Z-score ≤-2.0) at diagnosis, as an important indicator for fracture risk and further treatment-related BMD aggravation. Methods Children treated for ALL according to the Dutch Childhood Oncology Group (DCOG-ALL9; model development) protocol (n=249; median age: 7.6 years [range: 4.0-16.6 years]) and children from the Canadian STeroid-Associated Osteoporosis in the Pediatric Population (STOPP; model validation) cohort (n=99; median age: 7.3 years [range: 4.0-16.6 years]) were included in this study. Multivariable logistic regression analyses were used to develop the prediction model for low LS BMD at diagnosis, defined as a Z-score ≤-2.0 (evaluated with DXA). Candidate predictors included sex, age, height and weight Z-scores at diagnosis of ALL. The receiver operating characteristic area under the curve (AUC) was assessed for model performance. To confirm the association between low LS BMD at diagnosis and bone fragility during and shortly following ALL therapy, we performed multivariable logistic regression analyses. The dependent variables were: one or more symptomatic fractures from ALL diagnosis to 12 months following treatment cessation and low LS BMD at cessation of treatment. In addition, because of homogeneity in the intended glucocorticoid doses, we combined data from the DCOG-ALL9 and STOPP cohorts and performed multivariable pooled cohort analyses (meta-analysis). Potential associations between the six-month cumulative glucocorticoid dose and fractures that occurred in the first year of therapy, were explored. Furthermore, we assessed potential associations between the cumulative glucocorticoid dose at cessation of therapy, and the endpoints 'low LS BMD at therapy cessation' and 'fractures that occurred during treatment and within 12 months following treatment cessation'. Results The prediction model for low LS BMD at diagnosis included weight Z-scores (β = -0.70) and age (β = -0.10) at diagnosis. This model had an AUC of 0.71 (0.63 to 0.78) in the DCOG-ALL9 cohort, and resulted in correct identification of 71% of patients with low LS BMD at ALL diagnosis. Validation on the STOPP cohort showed an AUC of 0.74 (95% CI = 0.63 to 0.84). To calculate the probability of low LS BMD at ALL diagnosis for an individual patient, an online calculator is available at http://lsbmd-risk-calculator.azurewebsites.net/ We confirmed that low LS BMD at diagnosis is associated with LS BMD at treatment cessation (OR = 5.9; 95% CI = 3.2 to 10.9) and with symptomatic fractures (OR = 1.7; 95% CI = 1.3 to 2.4) that occurred from diagnosis until 12 months following treatment cessation. In pooled meta-analysis, lower LS BMD at diagnosis (OR = 1.6, 95% CI = 1.1 to 2.4) and six-month cumulative glucocorticoid dose (OR = 1.9, 95% CI = 1.1 to 3.3, for every gram increase) were associated with symptomatic fractures that occurred in the first year of therapy. Higher cumulative glucocorticoid dose at cessation of therapy (OR = 1.5, 95% CI = 1.2 to 2.0, for every gram increase), lower LS BMD Z-scores at diagnosis (OR = 7.9, 95% CI = 4.8 to 13.1) and higher age at diagnosis (OR = 1.6, 95% CI = 1.4 to 1.8), were associated with low LS BMD at cessation of therapy. Conclusion We developed and successfully validated a risk prediction model for low LSBMD at diagnosis in children aged 4-18 years with ALL. This is important because low LS BMD at diagnosis was strongly associated with bone fragility and fractures during and shortly following treatment for ALL. Our easy to use prediction model, can facilitate awareness and early identification of bone fragility in individual pediatric ALL patients, without performing DXA examination. Disclosures No relevant conflicts of interest to declare.


Author(s):  
Maddalena Giannella ◽  
Maristela Freire ◽  
Matteo Rinaldi ◽  
Edson Abdala ◽  
Arianna Rubin ◽  
...  

Abstract Background Patients colonized with carbapenem resistant Enterobacteriaceae (CRE) are at higher risk of developing CRE infection after liver transplantation (LT) with associated high morbidity and mortality. Prediction model for CRE infection after LT among carriers could be useful to target preventive strategies. Methods Multinational multicenter cohort study of consecutive adult patients underwent LT and colonized with CRE before or after LT, from January 2010 to December 2017. Risk factors for CRE infection were analyzed by univariate analysis and by Fine-Gray sub-distribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created. Results 840 LT recipients found to be colonized with CRE before (n=203) or after (n=637) LT were enrolled. CRE infection was diagnosed in 250 (29.7%) patients within 19 (IQR 9-42) days after LT. Pre-and post-LT colonization, multisite post-LT colonization, prolonged mechanical ventilation, acute renal injury, and surgical re-intervention were retained in the prediction model. Median 30 and 60-day predicted risk was 15% (IQR 11-24%) and 21% (IQR 15-33%), respectively. Discrimination and prediction accuracy for CRE infection was acceptable on derivation (AUC 74.6, Brier index 16.3) and bootstrapped validation dataset (AUC 73.9, Brier index 16.6). Decision-curve analysis suggested net benefit of model-directed intervention over default strategies (treat all, treat none) when CRE infection probability exceeded 10%. The risk prediction model is freely available as mobile application at https://idbologna.shinyapps.io/CREPostOLTPredictionModel/. Conclusions Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant.


2021 ◽  
Author(s):  
Hannah Moshontz ◽  
Alejandra J Colmenares ◽  
Gaylen E Fronk ◽  
Sarah J Sant'Ana ◽  
Kendra Wyant ◽  
...  

BACKGROUND Successful long-term recovery from opioid use disorder (OUD) requires continuous lapse risk monitoring and appropriate use and adaptation of recovery-supportive behaviors as lapse risk changes. Available treatments often fail to support long-term recovery by failing to account for the dynamic nature of long-term recovery. OBJECTIVE The aim of this protocol paper is to describe research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse. METHODS The participants will include 480 US adults in their first year of recovery from OUD. Participants will report lapses and provide data relevant to lapse risk for a year with a digital therapeutic smartphone app through both self-report and passive personal sensing methods (eg, cellular communications and geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data. RESULTS The National Institute of Drug Abuse funded this project (R01DA047315) on July 18, 2019 with a funding period from August 1, 2019 to June 30, 2024. The University of Wisconsin-Madison Health Sciences Institutional Review Board approved this project on July 9, 2019. Pilot enrollment began on April 16, 2021. Full enrollment began in September 2021. CONCLUSIONS The model that will be developed in this project could support long-term recovery from OUD—for example, by enabling just-in-time interventions within digital therapeutics. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/29563


2021 ◽  
Author(s):  
Hannah Moshontz ◽  
Alejandra Jose Colmenares ◽  
Gaylen Fronk ◽  
Sarah June Kittleson Sant'Ana ◽  
Kendra Wyant ◽  
...  

Successful long-term recovery from Opioid Use Disorder requires continuous lapse risk monitoring and appropriately using and adapting recovery-supportive behaviors as lapse risk changes. Available treatments often fail to support long-term recovery by failing to account for the dynamic nature of long-term recovery. This protocol paper describes research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse. Participants will be 480 American adults in their first year of recovery from Opioid Use Disorder. Participants will report lapses and provide data relevant to lapse risk for a year with a digital therapeutic smartphone app, through both self-report and passive personal sensing methods (e.g., cellular communications, geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data. The model this project will develop could support long-term recovery from Opioid Use Disorder, for example, by enabling just-in-time interventions within digital therapeutics. This project is funded by the National Institute on Drug Abuse with a funding period from August 2019 to June 2024. Full enrollment began in September 2021.


Author(s):  
Nuur Azreen Paiman ◽  
◽  
Azian Hariri ◽  
Ibrahim Masood ◽  
Arma Noor ◽  
...  

2021 ◽  
Vol 79 ◽  
pp. S1112-S1113
Author(s):  
A.A. Nasrallah ◽  
M. Mansour ◽  
C.H. Ayoub ◽  
N. Abou Heidar ◽  
J.A. Najdi ◽  
...  

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