Abstract
ObjectiveTo develop a customized short LOS (<6 days) prediction model for geriatric patients receiving cardiac surgery, using local data and a computational feature selection algorithm.DesignUtilization of a machine learning algorithm in a prospectively collected STS database consisting of patients who received cardiac surgery between January 2002 and June 2011.SettingUrban tertiary-care center.ParticipantsGeriatric patients aged 70 years or older at the time of cardiac surgery.InterventionsNone.Measurements and Main ResultsPredefined morbidity and mortality events were collected from the STS database. 23 clinically relevant predictors were investigated for short LOS prediction with a genetic algorithm (GenAlg) in 1426 patients. Due to the absence of an STS model for their particular surgery type, STS risk scores were unavailable for 771 patients. STS prediction achieved an AUC of 0.629 while the GenAlg achieved AUCs of 0.573 (in those with STS scores) and 0.691 (in those without STS scores). Among the patients with STS scores, the GenAlg features significantly associated with shorter LOS were absence of congestive heart failure (CHF) (OR = 0.59, p = 0.04), aortic valve procedure (OR = 1.54, p = 0.04), and shorter cross clamp time (OR = 0.99, p = 0.004). In those without STS prediction, short LOS was significantly correlated with younger age (OR = 0.93, p < 0.001), absence of CHF (OR = 0.53, p = 0.007), no preoperative use of beta blockers (OR = 0.66, p = 0.03), and shorter cross clamp time (OR = 0.99, p < 0.001).ConclusionWhile the GenAlg-based models did not outperform STS prediction for patients with STS risk scores, our local-data-driven approach reliably predicted short LOS for cardiac surgery types that do not allow STS risk calculation. We advocate that each institution with sufficient observational data should build their own cardiac surgery risk models.
Highlights
With aging population, more and more geriatric patients with complex co-morbid conditions at increased risk for morbidity and mortality present for cardiac surgery [1]
Measurements and Main Results: Predefined morbidity and mortality events were collected from the Society of Thoracic Surgeons (STS) database. 23 clinically relevant predictors were investigated for short length of stay (LOS) prediction with a genetic algorithm (GenAlg) in 1426 patients
We obtained the records from the Society of Thoracic Surgeons (STS) database maintained by trained personnel at our institution since 2001
Summary
More and more geriatric patients with complex co-morbid conditions at increased risk for morbidity and mortality present for cardiac surgery [1] Risk scores such as EuroSCORE [2] and the Society of Thoracic Surgeons (STS) risk score [3] are commonly used to prognosticate the risk of postoperative morbidity and mortality. These scores, having been derived from a large, heterogeneous population to optimize external validity, tend to perform well at the population level but not as well at individual level. Such sub-optimal predictive accuracy at the individual level could be attributed to the event (mortality) rates in the 10% - 15% range [5]. In isolated aortic valve replacement (AVR) in octogenarians, the actual mortality rates were as low as 5% while the predicted rates by EuroSCORE and STS risk scores were three to four folds higher [6]
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