Predictive model for prolonged length of hospital stay in patients with osteoporotic femoral neck fracture: A 5-year retrospective study

Abstract

Prolonged length of stay (LOS) in osteoporotic femoral neck fracture patients increased the hospital care cost and demonstrated in-hospital complications. This study aimed to develop an ease-of use predictive model of prolonged LOS in osteoporotic femoral neck fracture patients. In this 5-year retrospective study, the medical charts of 255 patients admitted to hospital with an osteoporotic femoral neck fracture resulting from a simple fall from January 2014 to December 2018 were reviewed. Multivariable fractional polynomials (MFP) algorithms was applied to develop the predictive model from candidate predictors of prolonged LOS. The discrimination performance of predictive model was evaluated using the receiver operating characteristic curve (ROC). Internal validity was assessed using bootstrapping. From 289 patients who were hospitalized with an osteoporotic fracture of femoral neck throughout this study, 255 (88%) fulfilled the inclusion criteria. There was 54.90% (140 of 255 patients) of patients who had prolonged LOS. The predictors of the predictive model were age, BMI, ASA score class 3 or 4, arthroplasty and time from injury to surgery. The area under ROC curve of the model was 0.83 (95% confidence interval 0.77–0.88). Internal validation with bootstrap re-sampling revealed an optimism of −0.002 (range −0.300–0.296) with an estimated shrinkage factor of 0.907 for the predictive model. The current predictive model developed from preoperative predictors which had a good discriminative ability to differentiate between length of hospitalization less than 14 days and prolonged LOS in osteoporotic femoral neck patients. This model can be applied as ease-of use calculator application to help patients, their families and clinicians make appropriate decisions in terms of treatment planning, postoperative care program, and cost-effectiveness before patients receiving the definitive treatments.

Length of hospital stay and associated factors among adult surgical patients admitted to a surgical ward in Amhara Regional State Comprehensive Specialized Hospitals, Ethiopia

“…On the other hand, this study result is lower than the study conducted in Nigeria (63%) [17] and Thailand (54.9%) [52]. In the case of Nigeria, this discrepancy may be due to the study period and the lower cutoff points used, which were at least 7 days, which is less than the cutoff point used in this study.…”

Application of machine learning models on predicting the length of hospital stay in fragility fracture patients

“…In those studies, the American Society of Anesthesiologists physical status classification system was used to classify the physical status while our study used the MFAC score to categorize functional ambulation ability. Recently, a similar study [25] predicted LOS in pre-operative femoral neck fracture patients using machine learning techniques and they concluded that the age, ASA score, BMI, and time from injury to surgery were strong predictors of prolonged LOS. Their results were mostly compatible with our findings -we also discovered that age and physical status, reflected by MFAC, were strong predictors of prolonged LOS across various high-performing models.…”

“…Unique to our study, we have data attributes that are not commonly found in other geriatric fragility fracture databases. Most of the studies done on geriatric fragility fracture only have basic data features, such as gender and age [22][23][24][25], and some easily attainable data [24,25], such as height, weight, and the International Classification of Disease (10th Revision) code, etc. Our study had more data features to more accurately reflect the situation of each holistically.…”

“…Also unique to our study, we did not observe the relationship between surgical delay and prolonged LOS. Several previous studies [25,[27][28][29] have identified surgical delay as a strong predictor of prolonged LOS, although there were studies suggesting otherwise [26,30]. In our database, our interpretation of surgical delay is defined as the date of first surgery minus the date of acute admission ('Surg_1-Acute_Adm'), which shows the duration between the patient being admitted to the acute hospital and receiving surgery on the fracture.…”