1915. Predicting real-time risk of complications in the postoperative setting with temperature as a single variable
Session: Poster Abstract Session: Clinical Practice Issues: HIV, Sepsis, QI, Diagnosis
Saturday, October 6, 2018
Room: S Poster Hall


No real-time postoperative risk stratification model exists to predict complications following surgery.  The aim of this work is to understand if we can successfully risk stratify patients across three distinct surgeries using group-based trajectory modeling (GBTM) with only a single variable, temperature.


We performed a retrospective study of adults undergoing elective total knee arthroplasty (TKA), total hip arthroplasty (THA), colectomy, and pancreatectomy at an academic medical center from 10/2014 to 2/2018.  Clinical data were abstracted using definitions from the National Surgical Quality Improvement Program (NSQIP) and temperature data were extracted from the Database Warehouse. GBTM was used to identify distinct clusters of patients with similar temperature trajectories. We calculated rates of complications and combined all NSQIP infectious and inflammatory complications into a single metric hence forth labeled inflammatory complications. Chi-square test was used to compare categorical variables.


We identified 815 independent surgical patients: 307 TKA/THA, 195 pancreatectomy, and 313 colectomy patients.  Rates of all NSQIP complications were 1.6% for TKA/THA, 35.4% for pancreatectomy and 10.2% for colectomy at 30 days after surgery. Pancreatectomy patients clustered into two temperature trajectories and both TKA/THA and colectomy patients (Figure 1) clustered into three groups. Inflammatory complication frequencies were significantly different in colectomy and trended towards significance for TKA/THA and pancreatectomy (Table 1).


Temperature trajectory modeling may help identify postoperative patients at higher risk for surgical complication after surgery. While risk stratification seems to work better in high complication surgeries or models with more patients the promise of this modeling technique relies on the ability to identify high risk patients with a single variable.

Figure 1: GBTM of temperature trajectories after colectomy

Table 1: Rates of inflammatory complications by temperature trajectory


Low risk (n)

Medium Risk (n)

High Risk (n)

P value


9.3% (150)

7.1% (140)

26.1% (23)



27.1% (118)


41.6% (77)



0.52% (194)

2.0% (99)

7.1% (14)



Katherine Kaplar, D.O.1,2, Urmila Ravichandran, MS3, Ronak Parikh, DO3, Eric Bhaimia, D.O.4, Elias Baied, DO5, Frances Lahrman, DO5, Huma Saeed, M.D.5, Jennifer Paruch, MD3, Rema Padman, PhD6, Nirav Shah, MD, MPH7 and Jennifer Grant, MD8, (1)Internal Medicine, NorthShore University HealthSystem, Evanston, IL, (2)University of Chicago, Chicago, IL, (3)NorthShore University HealthSystem, Evanston, IL, (4)Internal Medicine, Univeristy of Chicago (NorthShore), Evanston, IL, (5)Internal Medicine, University of Chicago (NorthShore), Evanston, IL, (6)Healthcare Informatics, Carnegie Mellon University, Pittsburgh, PA, (7)Infectious Diseases / Informatics, NorthShore University Health Systems, Evanston, IL, (8)Infectious Disease, NorthShore University HealthSystem, Evanston, IL


K. Kaplar, None

U. Ravichandran, None

R. Parikh, None

E. Bhaimia, None

E. Baied, None

F. Lahrman, None

H. Saeed, None

J. Paruch, None

R. Padman, None

N. Shah, None

J. Grant, None

Findings in the abstracts are embargoed until 12:01 a.m. PDT, Wednesday Oct. 3rd with the exception of research findings presented at the IDWeek press conferences.