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Development of a Machine Learning-Based Model for Predicting the Incidence of Peripheral Intravenous Catheter-Associated Phlebitis Cover

Development of a Machine Learning-Based Model for Predicting the Incidence of Peripheral Intravenous Catheter-Associated Phlebitis

Open Access
|Jul 2024

Figures & Tables

Fig. 1.

Patient PIVC flowchart (ICU, intensive care unit; PIVC, peripheral intravenous catheter)
Patient PIVC flowchart (ICU, intensive care unit; PIVC, peripheral intravenous catheter)

Fig. 2.

Importance of predictors in the random survival forest model. The variable importance was measured and scaled to have a maximum value of 100.
Importance of predictors in the random survival forest model. The variable importance was measured and scaled to have a maximum value of 100.

Fig. 3.

Receiver operating characteristic (ROC) curves of each model in the development set. (a) The c-statics (95% CI) of the models for time-to-event outcomes; random survival forest: 0.645 (0.606–0.684), and Cox proportional hazards model: 0.581 (0.542–0.621). The black and green lines represent the random survival forest and Cox proportional hazards models, respectively. (b) The c-statics (95% CI) of the models for binary outcomes: LASSO, 0.699 (0.662–0.736); random forest, 0.980 (0.973–0.986); gradient boosting tree, 0.892 (0.870–0.914); and logistic regression, 0.725 (0.688–0.762). The black, red, green, and blue lines represent LASSO, random forest, gradient boosting tree, and logistic regression, respectively. CI: Confidence interval
Receiver operating characteristic (ROC) curves of each model in the development set. (a) The c-statics (95% CI) of the models for time-to-event outcomes; random survival forest: 0.645 (0.606–0.684), and Cox proportional hazards model: 0.581 (0.542–0.621). The black and green lines represent the random survival forest and Cox proportional hazards models, respectively. (b) The c-statics (95% CI) of the models for binary outcomes: LASSO, 0.699 (0.662–0.736); random forest, 0.980 (0.973–0.986); gradient boosting tree, 0.892 (0.870–0.914); and logistic regression, 0.725 (0.688–0.762). The black, red, green, and blue lines represent LASSO, random forest, gradient boosting tree, and logistic regression, respectively. CI: Confidence interval

Fig. 4.

Receiver operating characteristic (ROC) curves of each model in the validation set. (a) The c-statics (95% CI) of the models for time-to-event outcomes: random survival forest, 0.655 (0.603–0.708); Cox Proportional Hazard Model, 0.516 (0.454–0.578). The black and green lines represent the random survival forest and Cox proportional hazards models, respectively. (b) The c-statics (95% CI) of the models for binary outcomes: LASSO, 0.680 (0.625–0.735); random forest, 0.677 (0.622–0.731); gradient boosting tree, 0.646 (0.587–0.706); and logistic regression, 0.633 (0.575–0.691). The black, red, green, and blue lines represent LASSO, random forest, gradient boosting tree, and logistic regression, respectively. CI: Confidence interval
Receiver operating characteristic (ROC) curves of each model in the validation set. (a) The c-statics (95% CI) of the models for time-to-event outcomes: random survival forest, 0.655 (0.603–0.708); Cox Proportional Hazard Model, 0.516 (0.454–0.578). The black and green lines represent the random survival forest and Cox proportional hazards models, respectively. (b) The c-statics (95% CI) of the models for binary outcomes: LASSO, 0.680 (0.625–0.735); random forest, 0.677 (0.622–0.731); gradient boosting tree, 0.646 (0.587–0.706); and logistic regression, 0.633 (0.575–0.691). The black, red, green, and blue lines represent LASSO, random forest, gradient boosting tree, and logistic regression, respectively. CI: Confidence interval

Patient characteristics of the development and validation cohorts at ICU admission

VariablesDevelopment cohort (N = 2,400)Validation cohort (N = 1,029)
Age, mean (SD), years68.1 (15.2)67.9 (15.0)
Sex, male (n, %)1485 (61.9)637 (61.9)
Body height, mean (SD), cm161 (9.6)161 (9.6)
Body weight, mean (SD), kg59.9 (15.4)60.1 (14.9)
BMI, mean (SD)23.0 (4.7)23.1 (4.5)
APACHE II, mean (SD)19.2 (8.3)19.2 (8.2)
SAPS II, mean (SD)44.0 (19.5)44.3 (19.0)
SOFA, mean (SD)6.8 (3.7)6.7 (3.7)
Charlson comorbidity index, mean (SD)4.2 (2.6)4.2 (2.6)

ICU admission from (n, %)
Operation room921 (38.4)427 (41.5)
Emergency room965 (40.2)404 (39.3)
General ward363 (15.1)142 (13.8)
Outpatients18 (0.8)2 (0.2)
Transfer from other hospital133 (5.5)54 (5.3)

Type of admission to the ICU (n, %)
Elective surgical478 (19.9)214 (20.8)
Emergency surgical443 (18.5)213 (20.7)
Medical1479 (61.6)602 (58.5)

ICU admission category (n, %)
Cardiology860 (35.8)341 (33.1)
Pulmonary350 (14.6)160 (15.6)
Gastrointestinal243 (10.1)100 (9.7)
Neurology455 (19.0)212 (20.6)
Trauma95 (4.0)41 (4.0)
Urology21 (0.9)10 (1.0)
Gynaecology16 (0.7)8 (0.8)
Skin/tissue33 (1.4)17 (1.7)
Others327 (13.6)140 (13.6)
Sepsis at ICU admission (n, %)495 (20.6)209 (20.3)
Mechanical ventilation within 24 hours after admission to ICU (n, %)1433 (59.7)631 (61.3)

PIVC characteristics during the insertion of the development and validation cohorts

VariablesDevelopment cohort (N = 2,400)Validation cohort (N = 1,029)
Catheter inserted by (n,%)
Doctor203/1,879 (10.8)81/801 (10.1)
Nurse1,673/1,879 (89.0)720/801 (89.9)

Inserted Site (n, %)
Upper arm245/2,378 (10.3)111/1,021 (10.9)
Forearm1,303/2,378 (54.8)546/1,021 (53.5)
Elbow113/2,378 4.8)50/1,021 (4.9)
Wrist118/2,378 5.0)44/1,021 (4.3)
Hand341/2,378 (14.3)166/1,021 (16.3)
Lower leg152/2,378 (6.4)73/1,021 (7.1)
Dorsal foot106/2,378 (4.5)31/1,021 (3.0)

Catheter material
PEU-Vialon*777/2,400 (32.4)310/1,029 (30.1)
Polyurethane658/2,400 (27.4)320/1,029 (31.1)
Polyethylene0/2,400 (0)0/1,029 (0)
Tetrafluoroethylene910/2,400 (37.9)382/1,029 (37.1)
Others55/2,400 (2.3)17/1,029 (1.7)

Catheter gauge (n,%)
14G1/2,357 (0.04%)0/1,011 (0)
16G51/2,357 (2.2)22/1,011 (2.2)
18G56/2,357 (2.4)33/1,011 (3.3)
20G612/2,357 (26.0)276/1,011 (27.3)
22G1,592/2,357 (67.5)662/1,011 (65.5)
24G45/2,357 (1.9)17/1,011 (1.7)

Antiseptic solution before catheterisation (n,%)
None5/1,863 (0.3)3/802 (0.4)
Alcohol1,817/1,863 (97.5)782/802 (97.5)
0.2% chlorhexidine alcohol14/1,863 (0.8)7/802 (0.9)
0.5% chlorhexidine alcohol10/1,863 (0.5)5/802 (0.6)
1.0% chlorhexidine alcohol12/1,863 (0.6)5/802 (0.6)
10% povidone iodine2/1,863 (0.1)0/802 (0)
other3/1,863 (0.2)0/802 (0)
Use of ultrasonography (n,%)36/1,844 (1.9)22/792 (2.8)

Number of trials for insertion (n,%)
11,501/1,834 (81.8)618/785 (79.7)
2221/1,834 (12.1)92/785 (11.7)
≥3112/1,834 (6.1)75/785 (9.6)

Difficulties with the insertions (n, %)
Easy882/1,811 (48.7)350/783 (44.7)
Slightly easy535/1,811 (29.5)237/783 (30.3)
Slightly difficult306/1,811 (16.9)150/783 (19.2)
Difficult88/1,811 (4.9)46/783 (5.9)

Glove (n,%)
Sterile14/1,836 (0.8)5/794 (0.6)
Non-sterile1,738/1,836 (94.7)758/794 (95.5)
Nothing84/1,836 (4.6)31/794 (3.9)

Dressing (n,%)
Chlorhexidine-impregnated dressing chrolehexidne0/2,377 (0)0/1,019 (0)
Sterile polyurethane dressing2,338/2,377 (98.4)989/1,019 (97.1)
Non-sterile polyurethane dressing polyuretane35/2,377 (1.5)25/1,019 (2.5)
Gauze dressing0/2,377 (0)1/1,019 (0.1)
Tape dressing4/2,377 (0.2)4/1,019 (0.4)
Any infection during catheter dwell (n, %)**550/2,400 (22.9)253/1,029 (24.6)
Duration of catheter dwell, median (IQR), hour44.7 (20.7–79.1)41.5 (21.0–76.5)
Phlebitis (n,%)208/2,400 (8.7)105/1,029 (10.2)

Difference of c-statistics in each model in the validation cohort

ModelC-statistics (95%CI)
Binary outcome models
LASSO0.680 (0.625–0.735)
Random forest0.677 (0.622–0.731)
Gradient boosting tree0.646 (0.587–0.706)
Logistic regression model0.633 (0.575–0.691)

Survival outcome models
Random survival forest0.655 (0.603–0.708)
Cox proportional hazards model0.516 (0.454–0.578)

Difference of c-statistics in each model in the development cohort

ModelC-statistics (95% CI)
Binary outcome models
LASSO0.699 (0.662–0.736)
Random forest0.980 (0.973–0.986)
Gradient boosting tree0.892 (0.870–0.914)
Logistic regression model0.725 (0.688–0.762)

Survival outcome models
Random survival forest0.645 (0.606–0.684)
Cox proportional hazards model0.581 (0.542–0.621)
DOI: https://doi.org/10.2478/jccm-2024-0028 | Journal eISSN: 2393-1817 | Journal ISSN: 2393-1809
Language: English
Page range: 232 - 244
Submitted on: Feb 7, 2024
Accepted on: Jun 15, 2024
Published on: Jul 31, 2024
Published by: University of Medicine, Pharmacy, Science and Technology of Targu Mures
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Hideto Yasuda, Claire M. Rickard, Olivier Mimoz, Nicole Marsh, Jessica A Schults, Bertrand Drugeon, Masahiro Kashiura, Yuki Kishihara, Yutaro Shinzato, Midori Koike, Takashi Moriya, Yuki Kotani, Natsuki Kondo, Kosuke Sekine, Nobuaki Shime, Keita Morikane, Takayuki Abe, published by University of Medicine, Pharmacy, Science and Technology of Targu Mures
This work is licensed under the Creative Commons Attribution 4.0 License.