Utility of preoperative blood screening before hip and knee arthroplasty

Machine Learning Model for the Prediction of Hemorrhage in Intensive Care Units

OBJECTIVES

Early hemorrhage detection in intensive care units (ICUs) enables timely intervention and reduces the risk of irreversible outcomes. In this study, we aimed to develop a machine learning model to predict hemorrhage by learning the patterns of continuously changing, real-world clinical data.

METHODS

We used the Medical Information Mart for Intensive Care databases (MIMIC-III and MIMIC-IV). A recurrent neural network was used to predict severe hemorrhage in the ICU. We developed three machine learning models with an increasing number of input features and levels of complexity: model 1 (11 features), model 2 (18 features), and model 3 (27 features). MIMIC-III was used for model training, and MIMIC-IV was split for internal validation. Using the model with the highest performance, external verification was performed using data from a subgroup extracted from the eICU Collaborative Research Database.

RESULTS

We included 5,670 ICU admissions, with 3,150 in the training set and 2,520 in the internal test set. A positive correlation was found between model complexity and performance. As a measure of performance, three models developed with an increasing number of features showed area under the receiver operating characteristic (AUROC) curve values of 0.61-0.94 according to the range of input data. In the subgroup extracted from the eICU database for external validation, an AUROC value of 0.74 was observed.

CONCLUSIONS

Machine learning models that rely on real clinical data can be used to predict patients at high risk of bleeding in the ICU.

The association of preoperative blood markers with postoperative readmissions following arthroplasty

AIMS

While preoperative bloodwork is routinely ordered, its value in determining which patients are at risk of postoperative readmission following total knee arthroplasty (TKA) and total hip arthroplasty (THA) is unclear. The objective of this study was to determine which routinely ordered preoperative blood markers have the strongest association with acute hospital readmission for patients undergoing elective TKA and THA.

METHODS

Two population-based retrospective cohorts were assembled for all adult primary elective TKA (n = 137,969) and THA (n = 78,532) patients between 2011 to 2018 across 678 North American hospitals using the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) registry. Six routinely ordered preoperative blood markers - albumin, haematocrit, platelet count, white blood cell count (WBC), estimated glomerular filtration rate (eGFR), and sodium level - were queried. The association between preoperative blood marker values and all-cause readmission within 30 days of surgery was compared using univariable analysis and multivariable logistic regression adjusted for relevant patient and treatment factors.

RESULTS

The mean TKA age was 66.6 years (SD 9.6) with 62% being females (n = 85,163/137,969), while in the THA cohort the mean age was 64.7 years (SD 11.4) with 54% being female (n = 42,637/78,532). In both cohorts, preoperative hypoalbuminemia (< 35 g/l) was associated with a 1.5- and 1.8-times increased odds of 30-day readmission following TKA and THA, respectively. In TKA patients, decreased eGFR demonstrated the strongest association with acute readmission with a standardized odds ratio of 0.75 per two standard deviations increase (p < 0.0001).

CONCLUSION

In this population level cohort analysis of arthroplasty patients, low albumin demonstrated the strongest association with acute readmission in comparison to five other commonly ordered preoperative blood markers. Identification and optimization of preoperative hypoalbuminemia could help healthcare providers recognize and address at-risk patients undergoing TKA and THA. This is the most comprehensive and rigorous examination of the association between preoperative blood markers and readmission for TKA and THA patients to date. Cite this article: Bone Jt Open 2021;2(6):388-396.