Introduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients... Show moreIntroduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. Methods We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. Results A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. Conclusion The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records. Show less
Plas-Krijgsman, W.G. van der; Boer, A.Z. de; Jong, P. de; Bastiaannet, E.; Bos, F. van den; Mooijaart, S.P.; ... ; Glas, N.A. de 2021
The number of older patients with breast cancer has increased due to the aging of the general population. The use of a geriatric assessment in this population has been advocated in many studies and... Show moreThe number of older patients with breast cancer has increased due to the aging of the general population. The use of a geriatric assessment in this population has been advocated in many studies and guidelines as it can be used to identify high risk populations for early mortality and toxicity. Additionally, geriatric parameters could predict relevant outcome measures. This systematic review summarizes all available evidence on predictive factors for various outcomes (disease-related and survival, toxicity, and patient-reported outcomes), with a special focus on geriatric parameters and patient-reported outcomes, in older patients with breast cancer. Studies were identified through systematic review of the literature published up to September 1st 2019 in the PubMed database and EMBASe. A total of 173 studies were included. Most studies investigated disease-related and survival outcomes (n = 123, 71%). Toxicity was investigated in 40 studies (23%) and a mere 15% (n = 26) investigated patient-reported outcomes. Various measures that can be derived from a geriatric assessment were predictive for survival endpoints. Furthermore, geriatric parameters were among the most frequently found predictors for toxicity and patient-reported outcomes. In conclusion, this study shows that geriatric parameters can predict survival, toxicity, and patient-reported outcomes in older patients with breast cancer. These findings can be used in daily clinical practice to identify patients at risk of early mortality, high risk of treatment toxicity or poor functional outcome after treatment. A minority of studies used relevant outcome measures for older patients, showing the need for studies that are tailored to the older population.(c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). Show less
