Abstract
BACKGROUND: The demand for percutaneous endoscopic gastrostomy (PEG) has increased because it is safe and a technically easy method, but it has risks of severe complications including death and a high mortality rate within 30 days. At present, we cannot predict survival or the incidence of complications before tube placement in an individual. Earlier studies have used traditional statistical analysis by assuming a linear relationship between clinical features, but most phenomena in the clinical situation are not linearly related. AIMS: We predicted the survival and complications before PEG placement in an individual by using artificial neural network (ANN) system, which can assess the nonlinear relationship. METHODS: We studied 100 patients who underwent PEG at the Kitasato Medical Institute Hospital from 1997 to 2005. Clinical data and laboratory data were used as input data. Complications related to PEG placement and survival dates were historically and prospectively measured. From the clinical data and laboratory data, we examined the prediction of outcome in individual patients using multiple logistic regression analysis and an ANN. RESULTS: The correct answer rate of survival by multiple logistic regression analysis was 67.9%. In contrast, using the ANN, we correctly predicted the survival date and aspiration pneumonia in 75 and 89% of patients, respectively. There was a nonlinear relationship among input factors and survival and complications. CONCLUSION: We correctly predicted the outcome and complications of individual patients with PEG with a high correct answer rate. Our data show the potential of an ANN as a powerful tool in daily clinical use to individualize treatment ('tailor-made medicine') for PEG and reduce costs.
| Original language | English |
|---|---|
| Pages (from-to) | 1279-1285 |
| Number of pages | 7 |
| Journal | European Journal of Gastroenterology and Hepatology |
| Volume | 21 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 2009 Nov |
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Keywords
- Artificial neural network
- Percutaneous endoscopic gastrostomy
- Prediction
- Survival
ASJC Scopus subject areas
- Gastroenterology
- Hepatology
Cite this
Prediction of survival and complications after percutaneous endoscopic gastrostomy in an individual by using clinical factors with an artificial neural network system. / Takayama, Tetsuro; Takayama, Kozo; Inoue, Nagamu; Funakoshi, Shinsuke; Serizawa, Hiroshi; Watanabe, Noriaki; Kumagai, Naoki; Tsuchimoto, Kanji; Hibi, Toshifumi.
In: European Journal of Gastroenterology and Hepatology, Vol. 21, No. 11, 11.2009, p. 1279-1285.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Prediction of survival and complications after percutaneous endoscopic gastrostomy in an individual by using clinical factors with an artificial neural network system
AU - Takayama, Tetsuro
AU - Takayama, Kozo
AU - Inoue, Nagamu
AU - Funakoshi, Shinsuke
AU - Serizawa, Hiroshi
AU - Watanabe, Noriaki
AU - Kumagai, Naoki
AU - Tsuchimoto, Kanji
AU - Hibi, Toshifumi
PY - 2009/11
Y1 - 2009/11
N2 - BACKGROUND: The demand for percutaneous endoscopic gastrostomy (PEG) has increased because it is safe and a technically easy method, but it has risks of severe complications including death and a high mortality rate within 30 days. At present, we cannot predict survival or the incidence of complications before tube placement in an individual. Earlier studies have used traditional statistical analysis by assuming a linear relationship between clinical features, but most phenomena in the clinical situation are not linearly related. AIMS: We predicted the survival and complications before PEG placement in an individual by using artificial neural network (ANN) system, which can assess the nonlinear relationship. METHODS: We studied 100 patients who underwent PEG at the Kitasato Medical Institute Hospital from 1997 to 2005. Clinical data and laboratory data were used as input data. Complications related to PEG placement and survival dates were historically and prospectively measured. From the clinical data and laboratory data, we examined the prediction of outcome in individual patients using multiple logistic regression analysis and an ANN. RESULTS: The correct answer rate of survival by multiple logistic regression analysis was 67.9%. In contrast, using the ANN, we correctly predicted the survival date and aspiration pneumonia in 75 and 89% of patients, respectively. There was a nonlinear relationship among input factors and survival and complications. CONCLUSION: We correctly predicted the outcome and complications of individual patients with PEG with a high correct answer rate. Our data show the potential of an ANN as a powerful tool in daily clinical use to individualize treatment ('tailor-made medicine') for PEG and reduce costs.
AB - BACKGROUND: The demand for percutaneous endoscopic gastrostomy (PEG) has increased because it is safe and a technically easy method, but it has risks of severe complications including death and a high mortality rate within 30 days. At present, we cannot predict survival or the incidence of complications before tube placement in an individual. Earlier studies have used traditional statistical analysis by assuming a linear relationship between clinical features, but most phenomena in the clinical situation are not linearly related. AIMS: We predicted the survival and complications before PEG placement in an individual by using artificial neural network (ANN) system, which can assess the nonlinear relationship. METHODS: We studied 100 patients who underwent PEG at the Kitasato Medical Institute Hospital from 1997 to 2005. Clinical data and laboratory data were used as input data. Complications related to PEG placement and survival dates were historically and prospectively measured. From the clinical data and laboratory data, we examined the prediction of outcome in individual patients using multiple logistic regression analysis and an ANN. RESULTS: The correct answer rate of survival by multiple logistic regression analysis was 67.9%. In contrast, using the ANN, we correctly predicted the survival date and aspiration pneumonia in 75 and 89% of patients, respectively. There was a nonlinear relationship among input factors and survival and complications. CONCLUSION: We correctly predicted the outcome and complications of individual patients with PEG with a high correct answer rate. Our data show the potential of an ANN as a powerful tool in daily clinical use to individualize treatment ('tailor-made medicine') for PEG and reduce costs.
KW - Artificial neural network
KW - Percutaneous endoscopic gastrostomy
KW - Prediction
KW - Survival
UR - http://www.scopus.com/inward/record.url?scp=70350166178&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350166178&partnerID=8YFLogxK
U2 - 10.1097/MEG.0b013e32832a4eae
DO - 10.1097/MEG.0b013e32832a4eae
M3 - Article
C2 - 19478677
AN - SCOPUS:70350166178
VL - 21
SP - 1279
EP - 1285
JO - European Journal of Gastroenterology and Hepatology
JF - European Journal of Gastroenterology and Hepatology
SN - 0954-691X
IS - 11
ER -