Applying Latent Class Analysis to Risk Stratification for Perioperative Mortality in Patients Undergoing Intraabdominal General Surgery.
Perioperative risk stratification is often performed using individual risk factors without consideration of the syndemic of these risk factors. We used latent class analysis (LCA) to identify the classes of comorbidities and risk factors associated with perioperative mortality in patients presenting for intraabdominal general surgery.



The 2005 to 2010 American College of Surgeons National Surgical Quality Improvement Program was used to obtain a cohort of patients undergoing intraabdominal general surgery. Risk factors and comorbidities were entered into LCA models to identify the latent classes, and individuals were assigned to a class based on the highest posterior probability of class membership. Relative risk regression was used to determine the associations between the latent classes and 30-day mortality, with adjustments for procedure.



A 9-class model was fit using LCA on 466,177 observations. After combining classes with similar adjusted mortality risks, 5 risk classes were obtained. Compared with the class with average mortality risk (class 4), the risk ratios (95% confidence interval) ranged from 0.020 (0.014-0.027) in the lowest risk class (class 1) to 6.75 (6.46-7.02) in the highest risk class. After adjusting for procedure and ASA physical status, the latent classes remained significantly associated with 30-day mortality. The addition of the risk class variable to a model containing ASA physical status and surgical procedure demonstrated a significant increase in the area under the receiver operator characteristic curve (0.892 vs 0.915; P < 0.0001).



Latent classes of risk factors and comorbidities in patients undergoing intraabdominal surgery are predictive of 30-day mortality independent of the ASA physical status and improve risk prediction with the ASA physical status.
From the *Department of Anesthesiology, Columbia University Medical Center, New York, New York; †Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York; and ‡Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.
Minjae
M
Kim
Melanie M
MM
Wall
Guohua
G
Li
D000005:Abdomen / Q000601:surgery*; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D015992:Body Mass Index; D015897:Comorbidity; D016208:Databases, Factual; D003661:Decision Support Techniques; D005260:Female; D006304:Health Status; D006801:Humans; D007813:Laparotomy / Q000009:adverse effects / Q000401:mortality*; D008297:Male; D008875:Middle Aged; D009656:North America; D059035:Perioperative Period; D011183:Postoperative Complications / Q000175:diagnosis / Q000209:etiology / Q000401:mortality*; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D013997:Time Factors; D016896:Treatment Outcome