Objectives: To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care,... Show moreObjectives: To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing homes.Study Design and Setting: This retrospective external validation study included 14,092 older individuals of >=70 years of age with a clinical or polymerase chain reaction-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, National Early Warning Score 2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation. Main Outcome Measure: In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting. Results: All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large 1.45 to 7.46, calibration slopes 0.24e0.81, and C-statistic 0.55e0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of 2.35 to 0.15 indicating overestimation, calibration slopes of 0.24e0.81 indicating signs of overfitting, and C-statistic of 0.55e0.71. Conclusion: Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic Show less
In this thesis the research of the role of new biomarkers in prognostic models in Cardiac surgery patients in the ICU is presented. The conclusion is that biomarkers, and especially nt-Pro-ADM, can... Show moreIn this thesis the research of the role of new biomarkers in prognostic models in Cardiac surgery patients in the ICU is presented. The conclusion is that biomarkers, and especially nt-Pro-ADM, can improve the accuracy of the EuroScore model and predict mortality equally good as the APACHE-IV model. A critical appraisal compares likewise studies with the studies published in the thesis. Also, the relation between genetic polymorphisms and serum levels of the biomarkers is investigated leading to the conclusion that for procalcitonin (PCT) serum levels depend on the genetic profile. Show less
Holl, D.C.; Mikolic, A.; Blaauw, J.; Lodewijkx, R.; Foppen, M.; Jellema, K.; ... ; Klaveren, D. van 2022
Background: Several prognostic models for outcomes after chronic subdural hematoma (CSDH) treatment have been published in recent years. However, these models are not sufficiently validated for use... Show moreBackground: Several prognostic models for outcomes after chronic subdural hematoma (CSDH) treatment have been published in recent years. However, these models are not sufficiently validated for use in daily clinical practice. We aimed to assess the performance of existing prediction models for outcomes in patients diagnosed with CSDH. Methods: We systematically searched relevant literature databases up to February 2021 to identify prognostic models for outcome prediction in patients diagnosed with CSDH. For the external validation of prognostic models, we used a retrospective database, containing data of 2384 patients from three Dutch regions. Prognostic models were included if they predicted either mortality, hematoma recurrence, functional outcome, or quality of life. Models were excluded when predictors were absent in our database or available for < 150 patients in our database. We assessed calibration, and discrimination (quantified by the concordance index C) of the included prognostic models in our retrospective database. Results: We identified 1680 original publications of which 1656 were excluded based on title or abstract, mostly because they did not concern CSDH or did not define a prognostic model. Out of 18 identified models, three could be externally validated in our retrospective database: a model for 30-day mortality in 1656 patients, a model for 2 months, and another for 3-month hematoma recurrence both in 1733 patients. The models overestimated the proportion of patients with these outcomes by 11% (15% predicted vs. 4% observed), 1% (10% vs. 9%), and 2% (11% vs. 9%), respectively. Their discriminative ability was poor to modest (C of 0.70 [0.63-0.77]; 0.46 [0.35-0.56]; 0.59 [0.51-0.66], respectively). Conclusions: None of the examined models showed good predictive performance for outcomes after CSDH treatment in our dataset. This study confirms the difficulty in predicting outcomes after CSDH and emphasizes the heterogeneity of CSDH patients. The importance of developing high-quality models by using unified predictors and relevant outcome measures and appropriate modeling strategies is warranted. Show less
In part 1 of the thesis Predicting Outcomes in Patients with Kidney Disease, key differences between etiological and prediction research are explored and it is shown that observational research... Show moreIn part 1 of the thesis Predicting Outcomes in Patients with Kidney Disease, key differences between etiological and prediction research are explored and it is shown that observational research often conflates etiology and prediction which leads to incorrect causal conclusions. A framework for the external validation of prognostic models is provided and it is shown how competing events can be dealt with when externally validating a time-to-event prognostic model. These results are applicable to many clinical research fields, including nephrology as exemplified in part 2. Within the six applied chapters in part 2, prediction models for various adverse outcomes in patients with advanced kidney disease are identified, validated and developed. The thesis provides a broad overview of prognostic model applications in patients with chronic kidney disease, including comprehensive external validation studies for kidney failure prediction models, mortality prediction models and graft failure prediction models. Models to predict mortality on conservative care and dialysis and models to predict adverse outcomes after kidney transplantation were developed and validated. These results may improve shared decision-making processes and individualized medicine for patients with kidney disease. Show less
Minne, L.; Eslami, S.; Keizer, N. de; Jonge, E. de; Rooij, S.E. de; Abu-Hanna, A. 2012
