Predicting 30-Day and 180-Day Mortality in Elderly Proximal Hip Fracture Patients: Evaluation of 4 Risk Prediction Scores at a Level I Trauma Center.
Arastoo NiaDomenik PoppGeorg ThalmannFabian GreinerNatasa JeremicRobert RusStefan HajduHarald Kurt WidhalmPublished in: Diagnostics (Basel, Switzerland) (2021)
This study evaluated the use of risk prediction models in estimating short- and mid-term mortality following proximal hip fracture in an elderly Austrian population. Data from 1101 patients who sustained a proximal hip fracture were retrospectively analyzed and applied to four models of interest: Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM), Charlson Comorbidity Index, Portsmouth-POSSUM and the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP®) Risk Score. The performance of these models according to the risk prediction of short- and mid-term mortality was assessed with a receiver operating characteristic curve (ROC). The median age of participants was 83 years, and 69% were women. Six point one percent of patients were deceased by 30 days and 15.2% by 180 days postoperatively. There was no significant difference between the models; the ACS-NSQIP had the largest area under the receiver operating characteristic curve for within 30-day and 180-day mortality. Age, male gender, and hemoglobin (Hb) levels at admission <12.0 g/dL were identified as significant risk factors associated with a shorter time to death at 30 and 180 days postoperative (p < 0.001). Among the four scores, the ACS-NSQIP score could be best-suited clinically and showed the highest discriminative performance, although it was not specifically designed for the hip fracture population.
Keyphrases
- hip fracture
- quality improvement
- cardiovascular events
- end stage renal disease
- patient safety
- acute coronary syndrome
- ejection fraction
- newly diagnosed
- emergency department
- peritoneal dialysis
- cardiovascular disease
- pregnant women
- middle aged
- patients undergoing
- mental health
- polycystic ovary syndrome
- kidney transplantation
- big data
- patient reported