Thromboembolic and major bleeding events in relation to perioperative bridging of vitamin K antagonists in 649 fast-track total hip and knee arthroplasties

Background — The benefit of preoperative bridging in surgical patients with continuous anticoagulant therapy is debatable, and drawing of meaningful conclusions may have been limited by mixed procedures with different thromboembolic and bleeding risks in most published studies. Patients and methods — This was an observational cohort treatment study in consecutive primary unilateral total hip and knee arthroplasty patients between January 2010 and November 2013 in 8 Danish fast-track departments. Data were collected prospectively on preoperative comorbidity and anticoagulants in patients with preoperative vitamin K antagonist (VKA) treatment. We performed 30-day follow-up on in-hospital complications and re-admissions through the Danish National Patient Registry and patient records. Results — Of 13,375 procedures, 649 (4.7%) were in VKA patients with a mean age of 73 (SD 9) years and a median length of stay of 3 days (IQR: 2–4). Preoperative bridging was used in 430 (67%), while 215 (33%) were paused. Of 4 arterial thromboembolic events (ATEs) (0.6%), 2 were in paused patients and 2 were in bridged patients (p = 0.6). Of 3 venous thromboembolic events (VTEs) (0.5%), 2 were in paused patients and 1 was in a bridged patient (p = 0.3). Of 8 major bleedings (MBs) (1.2%), 1 was in a paused patient and 7 were in bridged patients (p = 0.3), 5 of whom received therapeutic bridging. Similar results were found in a propensity-matched cohort. Interpretation — In contrast to recent studies in mixed surgical procedures, no statistically significant differences in ATE, VTE, or MB were found between preoperative bridging and pausation of VKA patients. However, the higher number of thromboembolic events in paused patients and the higher number of major bleedings in bridged patients warrant more extensive investigation

Does BMI influence hospital stay and morbidity after fast-track hip and knee arthroplasty?

BACKGROUND AND PURPOSE: Body mass index (BMI) outside the normal range possibly affects the perioperative morbidity and mortality following total hip arthroplasty (THA) and total knee arthroplasty (TKA) in traditional care programs. We determined perioperative morbidity and mortality in such patients who were operated with the fast-track methodology and compared the levels with those in patients with normal BMI. PATIENTS AND METHODS: This was a prospective observational study involving 13,730 procedures (7,194 THA and 6,536 TKA operations) performed in a standardized fast-track setting. Complete 90-day follow-up was achieved using national registries and review of medical records. Patients were grouped according to BMI as being underweight, of normal weight, overweight, obese, very obese, and morbidly obese. RESULTS: Median length of stay (LOS) was 2 (IQR: 2–3) days in all BMI groups. 30-day re-admission rates were around 6% for both THA (6.1%) and TKA (5.9%), without any statistically significant differences between BMI groups in univariate analysis (p > 0.4), but there was a trend of a protective effect of overweight for both THA (p = 0.1) and TKA (p = 0.06). 90-day re-admission rates increased to 8.6% for THA and 8.3% for TKA, which was similar among BMI groups, but there was a trend of lower rates in overweight and obese TKA patients (p = 0.08 and p = 0.06, respectively). When we adjusted for preoperative comorbidity, high BMI in THA patients (very obese and morbidly obese patients only) was associated with a LOS of >4 days (p = 0.001), but not with re-admission. No such relationship existed for TKA. INTERPRETATION: A fast-track setting resulted in similar length of hospital stay and re-admission rates regardless of BMI, except for very obese and morbidly obese THA patients

Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA).METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS &gt; 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values.RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication.CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.</p

Changes over time in characteristics, resource use and outcomes among ICU patients with COVID-19-A nationwide, observational study in Denmark

BACKGROUND: Characteristics and care of intensive care unit (ICU) patients with COVID‐19 may have changed during the pandemic, but longitudinal data assessing this are limited. We compared patients with COVID‐19 admitted to Danish ICUs in the first wave with those admitted later. METHODS: Among all Danish ICU patients with COVID‐19, we compared demographics, chronic comorbidities, use of organ support, length of stay and vital status of those admitted 10 March to 19 May 2020 (first wave) versus 20 May 2020 to 30 June 2021. We analysed risk factors for death by adjusted logistic regression analysis. RESULTS: Among all hospitalised patients with COVID‐19, a lower proportion was admitted to ICU after the first wave (13% vs. 8%). Among all 1374 ICU patients with COVID‐19, 326 were admitted during the first wave. There were no major differences in patient's characteristics or mortality between the two periods, but use of invasive mechanical ventilation (81% vs. 58% of patients), renal replacement therapy (26% vs. 13%) and ECMO (8% vs. 3%) and median length of stay in ICU (13 vs. 10 days) and in hospital (20 vs. 17 days) were all significantly lower after the first wave. Risk factors for death were higher age, larger burden of comorbidities (heart failure, pulmonary disease and kidney disease) and active cancer, but not admission during or after the first wave. CONCLUSIONS: After the first wave of COVID‐19 in Denmark, a lower proportion of hospitalised patients with COVID‐19 were admitted to ICU. Among ICU patients, use of organ support was lower and length of stay was reduced, but mortality rates remained at a relatively high level