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Table 2 Variable coefficients and overall model performance for each risk prediction model

From: Prediction of major postoperative events after non-cardiac surgery for people with kidney failure: derivation and internal validation of risk models

 

Model 1

Model 2

Model 3

Variable Name and Categories

OR (95%CI)

OR (95%CI)

OR (95%CI)

Age (per year above age 18)

1.03 (1.02, 1.03)

1.02 (1.01, 1.03)

1.02 (1.01, 1.03)

Female Sex

0.98 (0.86, 1.12)

1.02 (0.89, 1.17)

1.00 (0.87, 1.15)

Surgery Type

Intra-abdominal

Ref

Ref

Ref

 Head and Neck

1.01 (0.65, 1.57)

1.04 (0.67, 1.61)

1.09 (0.70, 1.69)

 Vascular

1.17 (0.91, 1.51)

1.07 (0.83, 1.38)

1.10 (0.85, 1.42)

 Skin and Soft Tissue

0.99 (0.74, 1.32)

0.89 (0.66, 1.19)

0.82 (0.61, 1.11)

 Neurosurgery

1.94 (1.16, 3.22)

2.04 (1.22, 3.41)

2.09 (1.25, 3.50)

 Peritoneal Dialysis Catheter

0.52 (0.38, 0.72)

0.56 (0.41, 0.78)

0.54 (0.39, 0.75)

 Arteriovenous Fistula

0.61 (0.47, 0.80)

0.60 (0.46, 0.79)

0.60 (0.46, 0.79)

 Kidney Transplant

0.28 (0.19, 0.43)

0.37 (0.24, 0.56)

0.42 (0.28, 0.65)

 Low risk – Othera

0.46 (0.32, 0.66)

0.44 (0.31, 0.64)

0.45 (0.31, 0.65)

 More than one type

1.30 (0.97, 1.75)

1.23 (0.91, 1.66)

1.19 (0.88, 1.62)

 Musculoskeletal

0.87 (0.67, 1.13)

0.84 (0.65, 1.10)

0.83 (0.64, 1.08)

Surgery Setting

 Ambulatory Surgery

Ref

Ref

Ref

 Major Elective

3.37 (2.68, 4.24)

3.40 (2.69, 4.28)

3.36 (2.66, 4.25)

 Major Urgent/Emergent

8.73 (7.55, 10.09)

7.69 (6.63, 8.92)

7.24 (6.23, 8.43)

Kidney Failure Type

 Hemodialysis

1.19 (1.00, 1.40)

1.06 (0.90, 1.27)

1.04 (0.87, 1.23)

 Peritoneal Dialysis

1.35 (1.06, 1.70)

1.34 (1.05, 1.70)

1.13 (0.88, 1.44)

 Non-dialysis

Ref

Ref

Ref

Comorbidities

 Cancer

 

1.28 (1.04, 1.57)

1.24 (1.01, 1.52)

 Cerebrovascular disease

 

1.14 (0.99, 1.32)

1.12 (0.97, 1.29)

 Chronic Pulmonary Disease

 

1.22 (1.06, 1.40)

1.23 (1.07, 1.42)

 Dementia

 

1.20 (0.95, 1.50)

1.15 (0.91, 1.45)

 Diabetes

 

1.22 (1.04, 1.43)

1.16 (0.99, 1.36)

 Heart Failure

 

1.68 (1.44, 1.96)

1.64 (1.41, 1.91)

 History of Myocardial Infarction

 

2.03 (1.72, 2.39)

2.02 (1.71, 2.38)

 Hypertension

 

1.11 (0.70, 1.78)

1.14 (0.72, 1.81)

 Liver disease

 

1.25 (0.87, 1.78)

1.15 (0.81, 1.63))

 Obesity

 

0.83 (0.70, 0.99)

0.83 (0.69, 0.99)

 Peripheral vascular disease

 

0.93 (0.80, 1.08)

0.87 (0.75, 1.01)

Serum Albumin (per one unit change in g/L)

  

0.95 (0.94, 0.96)

Serum Hemoglobin (per one unit change in g/L)

  

1.00 (1.00, 1.01)

Constant (baseline odds)

0.0041 (0.0028, 0.0060)

0.0030 (0.0016, 0.0054)

0.015 (0.0066, 0.035)

Model Fit and Performance

Number of surgeries included

38,541

38,541

38,541

Number of outcomes included

1,204

1,204

1,204

Akaike’s Information Criteria (AIC)

9237

8996

8934

Bayesian Information Criteria (BIC)

9383

9236

9192

Apparent Performance

 C-statistic (95%CI)

0.785 (0.771, 0.800)

0.813 (0.800, 0.826)

0.818 (0.806, 0.831)

 Area under precision recall curve

0.133

0.150

0.159

 Calibration Slope (95%CI)

1.00 (0.95, 1.05)

1.00 (0.95, 1.05)

1.00 (0.95, 1.05)

Optimism-adjusted Performance

 C-statistic (95%CI)

0.783 (0.770, 0.797)

0.809 (0.798, 0.823)

0.814 (0.803, 0.826)

 Expected to Observed Ratio (95%CI)

1.01 (0.97, 1.07)

1.01 (0.97, 1.07)

1.01 (0.97, 1.07)

 Calibration intercept (95%CI)

0.00 (-0.06, 0.06)

0.00 (-0.06, 0.06)

0.00 (-0.06, 0.06)

 Calibration slope (95%CI)

0.99 (0.94, 1.04)

0.98 (0.94, 1.03)

0.98 (0.94, 1.03)

Comparison with simpler nested model

Net Reclassification Index (NRI)

 Total (95% CI), p-value

-

0.095 (0.063, 0.12), p < 0.00001b

0.016 (-0.004, 0.035), p = 0.12c

 NARI (per 1000 patients)d

-

7.8

0.8

 Integrated Discrimination Index (IDI)

-

0.016, p < 0.00001b

0.004, p < 0.00001c

  1. CI confidence interval, OR odds ratio, Ref reference group for respective variable
  2. a Low-risk other surgery includes Anorectal, Breast, Lower Urologic and Gynecologic, Ophthalmology, Retroperitoneal, and Thoracic surgery types
  3. b Indicates that the comparison is between Model 2 and the more simple nested Model 1
  4. c Indicates that the comparison is between Model 3 and the more simple nested Model 2
  5. dNARI is the Net Absolute Reclassification Index in number per 1000 patients, and is calculated as: (Proportion of reclassification for patients with events x event rate) + (proportion reclassification for patients without events x non-event rate) × 1000