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

Preoperative hemoglobin thresholds for increased risk of "medical" complications in fast-track total hip and knee arthroplasty, a secondary analysis of a machine-learning algorithm

BACKGROUND

There is increasing evidence that gender-specific hemoglobin thresholds may not be ideal in the surgical population. Thus, preoperative anemia defined as a hemoglobin of <13.0 g/dL is a well-established risk factor in elective surgery. However, few studies have investigated the specific influence of preoperative hemoglobin within a machine-learning model using data from an optimized fast-track surgical setup.

STUDY DESIGN AND METHODS

A secondary analysis on the specific influence of preoperative hemoglobin level on a machine-learning model developed for identifying patients at increased risk of a length of stay (LOS) of >4 day or readmissions due to medical complications in fast-track total hip and knee arthroplasty within a well-defined fast-track protocol. To evaluate the effect of hemoglobin on the model we calculated SHaply Additive Explanation (SHAP) values for the 3913 patients from our previous test-dataset and stratified by gender and total hip and knee arthroplasty, respectively.

RESULTS

The study period ran from January 2017 to August 2017. Median LOS was 1 day and mean preoperative Hb was 15.5 g/dL (SD:1.5), lower in women (14.9 vs. 16.2 g/dL) and with 30.5% of women versus 12.0% of men having a Hb of <13.0 g/dL. There was a steep increase in SHAP value with a preoperative Hb < 14.8 g/dL, and irrespective of gender age and procedure type.

DISCUSSION

A machine-learning model found a hemoglobin threshold of <14.8 g/dL for increased risk of impaired recovery, regardless of gender or age, supporting reevaluation of preoperative anemia thresholds in the elective surgical setting.