Dataset Information

Generalizability of Dutch Prediction Models for Low Hemoglobin Deferral: A Study on External Validation and Updating in Swiss Whole Blood Donors.

ABSTRACT:

Background

Sex-specific prediction models for low hemoglobin (Hb) deferral have been developed in Dutch whole blood donors. In this study, we validated and updated the models in a cohort of Swiss whole blood donors.

Methods

Prospectively collected data from 53,772 Swiss whole blood donors were used. The predictive performance of the Dutch models was assessed in terms of calibration (agreement between predicted probabilities and observed frequencies) and discrimination (ability to discriminate between deferred and approved donors). The models were updated by revising the strength of the individual predictors in the models.

Results

A total of 1,065 men (3.3%) and 2,063 women (9.7%) were deferred from donation because of a low Hb level. Validation in Swiss donors demonstrated underestimation of predicted risks and significantly lower discriminative ability. The predictive effects of most predictors were weaker in Swiss donors. Updating the models increased the calibration for both men and women, and slightly increased the discriminative ability in men.

Conclusion

Validation of the Dutch prediction models in Swiss whole blood donors showed lower, though adequate performance. In general, the Dutch prediction models can reliably predict the risk of Hb deferral, although for application in other countries small adaptations are necessary.

SUBMITTER: Baart AM

PROVIDER: S-EPMC5159723 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Generalizability of Dutch Prediction Models for Low Hemoglobin Deferral: A Study on External Validation and Updating in Swiss Whole Blood Donors.

Baart A Mireille AM Fontana Stefano S Tschaggelar Anita A Heymans Martijn W MW de Kort Wim L A M WL

Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie 20161014 6

<h4>Background</h4>Sex-specific prediction models for low hemoglobin (Hb) deferral have been developed in Dutch whole blood donors. In this study, we validated and updated the models in a cohort of Swiss whole blood donors.<h4>Methods</h4>Prospectively collected data from 53,772 Swiss whole blood donors were used. The predictive performance of the Dutch models was assessed in terms of calibration (agreement between predicted probabilities and observed frequencies) and discrimination (ability to ...[more]

PMID: 27994527

Similar Datasets

Project description:BackgroundAccording to the Technical Operation Procedures for Plasmapheresis Collection Station (2019) in China, plasmapheresis donors with low hemoglobin (Hb) levels (men <12.0 g/dL; women <11.0 g/dL) were deferred for at least 2 weeks. The purpose of this retrospective study was to survey the demographic characteristics of plasmapheresis donors with low Hb deferral (LHD) and identify at-risk LHD donors, so as to enhance donor safety and improve donation service management.MethodsFrom 2018 to 2020, a multi-center study involving plasmapheresis donors from 18 plasmapheresis centers in three provinces (Sichuan, Yunnan and Hunan) of China was conducted. Donor demographics (age, sex) and donation information (date of donation, first-time donors vs. repeat donors, the number of lifetime donations, the number of donations in the last 12 months, and whether the LHD donor returned for a subsequent donation) were collected. The Cochran-Mantel-Haenszel method was used to explore the risk factors for LHD while adjusting for the different provinces. Logistic regression analysis was used to investigate the factors influencing the return for a subsequent donation after LHD.ResultsA total of 497,039 plasmapheresis donors were included. Female donors' LHD rate was 0.15% on average, while male donors' LHD rate was 0.01%. Female donors aged 41-50 years old (OR: 2.276, 95% CI [1.333-3.887], p = 0.002) were more likely to experience LHD temporarily than those aged 18-30 years old. For female donors, compared with donations in the winter, they had a higher risk for LHD in the summer (OR: 2.217, 95% CI [1.670-2.943], p < 0.001), spring (OR: 2.402, 95% CI [1.806-3.196], p < 0.001), and fall (OR: 2.002, 95% CI [1.500-2.673], p < 0.001). Among the LHD donors, those who had donated more frequently in the past were more likely to return for a subsequent donation (p = 0.012).ConclusionsFemale donors were at a higher risk of LHD, particularly between the ages of 41 and 50. A clear seasonal pattern in the rate of LHD was observed. In the winter, the risk of LHD was the lowest; thus, it was advised to recruit plasmapheresis donors throughout the winter and to make the required adjustments for recruitment measures during other seasons. The number of previous donations was correlated with the return rate after LHD. Our observations could have practical implications for plasmapheresis donor management.

Project description:PurposeFall prevention is a safety goal in many hospitals. The performance of the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) in older inpatients is largely unknown. We aimed to assess the JHFRAT performance in a large sample of Dutch older inpatients, including its trend over time.MethodsWe used an Electronic Health Records (EHR) dataset with hospitalized patients (≥ 70), admitted for ≥ 24 h between 2016 and 2021. Inpatient falls were extracted from structured and free-text data. We assessed the association between JHFRAT and falls using logistic regression. For test accuracy, we calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Discrimination was measured by the AUC. For calibration, we plotted the predicted fall probability with the actual probability of falls. For time-related effects, we calculated the AUC per 6 months (using data of patients admitted during the 6 months' time interval) and plotted these different AUC values over time. Furthermore, we compared the model (JHFRAT and falls) with and without adjusting for seasonal influenza, COVID-19, spring, summer, fall or winter periods.ResultsData included 17,263 admissions with at least 1 JHFRAT measurement, a median age of 76 and a percentage female of 47%. The in-hospital fall prevalence was 2.5%. JHFRAT [OR = 1.11 (1.03-1.20)] and its subcategories were significantly associated with falls. For medium/high risk of falls (JHFRAT > 5), sensitivity was 73%, specificity 51%, PPV 4% and NPV 99%. The overall AUC was 0.67, varying over time between 0.62 and 0.71 (for 6 months' time intervals). Seasonal influenza did affect the association between JHFRAT and falls. COVID-19, spring, summer, fall or winter did not affect the association.ConclusionsOur results show an association between JHFRAT and falls, a low discrimination by JHFRAT for older inpatients and over-prediction in the calibration. Improvements in the fall-risk assessment are warranted to improve efficiency.

Project description:Background:Understanding the demographic features of blood donors is important for identifying the donor structure and guiding the recruitment and retention strategies of donors. This study was aimed to determine the demographic characteristics of blood donors, the types of donation drives, and the frequency and reasons for pre-donation deferral in our population. Methods:This cross-sectional study included data analysis of all prospective blood donors presenting themselves for donation at the Blood Bank of the King Fahad Military Medical Complex, Dhahran, Saudi Arabia between January 2010 and December 2018. Eligibility of blood donation was assessed using a standardized history questionnaire, physical examination, hemoglobin estimation, pulse rate, blood pressure, and temperature. The questionnaire included the demographic features of the donor such as age, gender, and the reason for donation and other specific risk factors that potentially affect the safety of the donor or the recipient. Data analyzed using the analysis of SPSS version 20 (SPSS Inc., Chicago, IL, USA) were used to calculate the unadjusted odds ratios (OR) and the respective 95% confidence interval for the association between the various demographic parameters. Results:A total of 28,189 potential donations were recorded during the study period; of these, 77.4% were family replacements with females accounting for only 1.8% of all donations. Most of the donors were Saudi nationals (92.1%) of whom donors between 18 and 34 years old constituted 87.9%, while those over 40 years old represented only 7.9% of the donations. Pre-donation temporary deferral occurred in 3300 (11.7%) of all potential donation attempts. The most common causes being the recent intake of medication (24.4%) followed by low hemoglobin (14.8%), low or high blood pressure (14.6%), low or high pulse rate (13.6%), and low weight (11.6%). Conclusion:A profile of blood donors consisting mainly of young male citizens who mostly donated for family replacement was detected. Deferral rate was found to be average when compared to other countries; the causes being similar to those detected in other developed countries. Simple descriptive statistics has shown no marked difference was noted over the 9 years of the study in neither the profile of blood donors nor the cause for temporary deferral of prospective blood donors.

Project description:People tend to update beliefs about their future outcomes in a valence-dependent way: they are likely to incorporate good news and to neglect bad news. However, belief formation is a complex process which depends not only on motivational factors such as the desire for favorable conclusions, but also on multiple cognitive variables such as prior beliefs, knowledge about personal vulnerabilities and resources, and the size of the probabilities and estimation errors. Thus, we applied computational modeling in order to test for valence-induced biases in updating while formally controlling for relevant cognitive factors. We compared biased and unbiased Bayesian models of belief updating, and specified alternative models based on reinforcement learning. The experiment consisted of 80 trials with 80 different adverse future life events. In each trial, participants estimated the base rate of one of these events and estimated their own risk of experiencing the event before and after being confronted with the actual base rate. Belief updates corresponded to the difference between the two self-risk estimates. Valence-dependent updating was assessed by comparing trials with good news (better-than-expected base rates) with trials with bad news (worse-than-expected base rates). After receiving bad relative to good news, participants' updates were smaller and deviated more strongly from rational Bayesian predictions, indicating a valence-induced bias. Model comparison revealed that the biased (i.e., optimistic) Bayesian model of belief updating better accounted for data than the unbiased (i.e., rational) Bayesian model, confirming that the valence of the new information influenced the amount of updating. Moreover, alternative computational modeling based on reinforcement learning demonstrated higher learning rates for good than for bad news, as well as a moderating role of personal knowledge. Finally, in this specific experimental context, the approach based on reinforcement learning was superior to the Bayesian approach. The computational validation of valence-dependent belief updating represents a novel support for a genuine optimism bias in human belief formation. Moreover, the precise control of relevant cognitive variables justifies the conclusion that the motivation to adopt the most favorable self-referential conclusions biases human judgments.

Project description:Background Offering living kidney donation raised the concern that donors are exposed to unknown risks. All Swiss transplant centres therefore decided to start a prospective cohort study of living kidney donors in Switzerland. This paper describes the rationale for and implementation of this cohort study. Methods/design All kidney donors in Switzerland are registered and examined before donation and biennially after donation starting in the first year after nephrectomy. Before each follow-up visit, the study centre sends a package to the kidney donor containing the health questionnaire, blood and urine tubes and a prepaid envelope for sending the samples to the central laboratory. The donor makes an appointment with their family physician, who examines the donor and reports findings such as pain and other complaints, blood pressure, creatinine, albumin, all major health events and the state of mental and social well-being to the study centre. The family doctor draws the blood sample and mails it with the urine sample in the prepaid envelope. All data are centrally managed. All abnormal findings in the follow-up of individual donors are regularly discussed with the principal investigator, and necessary clinical changes made and recorded in the database. The health insurance of the recipient covers all costs of the donor follow-up. The main outcomes are the occurrence of albuminuria, hypertension and renal insufficiency. The secondary outcomes are major somatic and social events such as death, cardiovascular disease, stroke and depression. Discussion This prospective cohort offers unique opportunities to assess the risks of living kidney donation and will allow us to examine the risks associated with the methods used for nephrectomy in Switzerland (various forms of open surgery and laparoscopic nephrectomy). The prospective collection of all clinically relevant data and the regular monitoring of donors will allow timely interventions at early stages before serious kidney and general health problems occur.

Dataset Information

Generalizability of Dutch Prediction Models for Low Hemoglobin Deferral: A Study on External Validation and Updating in Swiss Whole Blood Donors.

Background

Methods

Results

Conclusion

Publications

Generalizability of Dutch Prediction Models for Low Hemoglobin Deferral: A Study on External Validation and Updating in Swiss Whole Blood Donors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets