TY - JOUR
T1 - NLP-Driven Analysis of Pneumothorax Incidence Following Central Venous Catheter Procedures
T2 - A Data-Driven Re-Evaluation of Routine Imaging in Value-Based Medicine
AU - Breitwieser, Martin
AU - Moore, Vanessa
AU - Wiesner, Teresa
AU - Wichlas, Florian
AU - Deininger, Christian
N1 - alle: Department for Orthopedic Surgery and Traumatology, Paracelsus Medical University, 5020 Salzburg, Austria
PY - 2024/12
Y1 - 2024/12
N2 - Background: This study presents a systematic approach using a natural language processing (NLP) algorithm to assess the necessity of routine imaging after central venous catheter (CVC) placement and removal. With pneumothorax being a key complication of CVC procedures, this research aims to provide evidence-based recommendations for optimizing imaging protocols and minimizing unnecessary imaging risks. Methods: We analyzed electronic health records from four university hospitals in Salzburg, Austria, focusing on X-rays performed between 2012 and 2021 following CVC procedures. A custom-built NLP algorithm identified cases of pneumothorax from radiologists' reports and clinician requests, while excluding cases with contraindications such as chest injuries, prior pneumothorax, or missing data. Chi-square tests were used to compare pneumothorax rates between CVC insertion and removal, and multivariate logistic regression identified risk factors, with a focus on age and gender. Results: This study analyzed 17,175 cases of patients aged 18 and older, with 95.4% involving CVC insertion and 4.6% involving CVC removal. Pneumothorax was observed in 106 cases post-insertion (1.3%) and in 3 cases post-removal (0.02%), with no statistically significant difference between procedures (p = 0.5025). The NLP algorithm achieved an accuracy of 93%, with a sensitivity of 97.9%, a specificity of 87.9%, and an area under the ROC curve (AUC) of 0.9283. Conclusions: The findings indicate no significant difference in pneumothorax incidence between CVC insertion and removal, supporting existing recommendations against routine imaging post-removal for asymptomatic patients and suggesting that routine imaging after CVC insertion may also be unnecessary in similar cases. This study demonstrates how advanced NLP techniques can support value-based medicine by enhancing clinical decision making and optimizing resources.
AB - Background: This study presents a systematic approach using a natural language processing (NLP) algorithm to assess the necessity of routine imaging after central venous catheter (CVC) placement and removal. With pneumothorax being a key complication of CVC procedures, this research aims to provide evidence-based recommendations for optimizing imaging protocols and minimizing unnecessary imaging risks. Methods: We analyzed electronic health records from four university hospitals in Salzburg, Austria, focusing on X-rays performed between 2012 and 2021 following CVC procedures. A custom-built NLP algorithm identified cases of pneumothorax from radiologists' reports and clinician requests, while excluding cases with contraindications such as chest injuries, prior pneumothorax, or missing data. Chi-square tests were used to compare pneumothorax rates between CVC insertion and removal, and multivariate logistic regression identified risk factors, with a focus on age and gender. Results: This study analyzed 17,175 cases of patients aged 18 and older, with 95.4% involving CVC insertion and 4.6% involving CVC removal. Pneumothorax was observed in 106 cases post-insertion (1.3%) and in 3 cases post-removal (0.02%), with no statistically significant difference between procedures (p = 0.5025). The NLP algorithm achieved an accuracy of 93%, with a sensitivity of 97.9%, a specificity of 87.9%, and an area under the ROC curve (AUC) of 0.9283. Conclusions: The findings indicate no significant difference in pneumothorax incidence between CVC insertion and removal, supporting existing recommendations against routine imaging post-removal for asymptomatic patients and suggesting that routine imaging after CVC insertion may also be unnecessary in similar cases. This study demonstrates how advanced NLP techniques can support value-based medicine by enhancing clinical decision making and optimizing resources.
KW - Cvc
KW - Nlp
KW - Central venous catheterization
KW - Machine learning
KW - Natural language processing
KW - Pneumothorax
KW - Removal
KW - Value-based medicine
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pmu_pure&SrcAuth=WosAPI&KeyUT=WOS:001384925200001&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.3390/diagnostics14242792
DO - 10.3390/diagnostics14242792
M3 - Original Article
C2 - 39767153
SN - 2075-4418
VL - 14
JO - DIAGNOSTICS
JF - DIAGNOSTICS
IS - 24
M1 - 2792
ER -