Improving the care for elective surgical patients: post-operative ICU admission and outcome

Cutuli, Salvatore Lucio; Carelli, Simone; Pascale, Gennaro De; Antonelli, Massimo

doi:10.21037/jtd.2018.04.64

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…Although presenting some limitations, the study by Taccone et al 46 made it clear that routine postoperative admission to ICUs is not helpful “per se”. The authors underline the need for tailored postoperative admission based on the risk factor of each individual patient, and thus stress the importance of accurate postoperative risk prediction 47 .…”

Section: Discussionmentioning

confidence: 99%

The new SUMPOT to predict postoperative complications using an Artificial Neural Network

Chelazzi

Villa

Manno

et al. 2021

Sci Rep

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An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohort of 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units, high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy in detecting those patients who will develop postoperative complications. A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified). The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirm its applicability in routine clinical practice.

show abstract

Section: Discussionmentioning

confidence: 99%

The new SUMPOT to predict postoperative complications using an Artificial Neural Network

Chelazzi

Villa

Manno

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Although presenting some limitations, the study by Taccone et al 57 made it clear that routine postoperative admission to ICUs is not helpful "per se". The authors underline the need for tailored postoperative admission based on the risk factor of each individual patient, and thus stress the importance of accurate postoperative risk prediction 17 .…”

Section: Discussionmentioning

confidence: 99%

The New SUMPOT to Predict Postoperative Complications Using an Artificial Neural Network

Chelazzi

Villa

Manno

et al. 2021

Preprint

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An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality inhigh-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial NeuralNetwork technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohortof 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units,high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, anda testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of theaccuracy in detecting those patients who will develop postoperative complications.A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperativecomplications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classificationaccuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified).The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications,suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirmits applicability in routine clinical practice.

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“…3 The WHO defines surgery as "an essential component of healthcare worldwide, and often the only treatment option that can alleviate disability and reduce the risk of death from the condition". 4 Postoperative complications are also one of the health problems that cause about 3-12 million deaths yearly.…”

Section: Introductionmentioning

confidence: 99%

Post Operative Management in Intensive Care of Obstructive Jaundice caused by Choledocholithiasis: A Case Report

Kahar,

Wiwi Monika Sari

2023

Ina J Rheum

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Introduction: Gallstones are a very common problem in the global population. 10%-25% of affected people may have specific symptoms, and 1%-2% of them may experience major complications. Case presentation: 83-year-old female patient complained of yellow in her eyes and skin. Patients also felt pain in the right upper abdomen. Laboratory examination revealed increased bilirubin serum. Abdominal Ultrasound examination showed Obstruction of the Bilier Ductus caused by a stone at ductus sisticus and sludge of the gallbladder. The patient was diagnosed with Jaundice obstruction caused by obstruction of the bile ducts due to a stone at the ductus sisticus with a history of nephrectomy, Hypertension, and type 2 diabetes. The patient underwent Laparoscopic cholecystectomy, ERCP, and stent biller, After the procedure, the patient was admitted to the intensive care unit. Conclusion: Understanding the patient's comprehensive medical and surgical history is fundamental in understanding the postoperative patient in the intensive care unit. Adequate resuscitation is necessary for good clinical perfusion and physiological stability of the patient.

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Improving the care for elective surgical patients: post-operative ICU admission and outcome

Cited by 8 publications

References 18 publications

The new SUMPOT to predict postoperative complications using an Artificial Neural Network

The new SUMPOT to predict postoperative complications using an Artificial Neural Network

The New SUMPOT to Predict Postoperative Complications Using an Artificial Neural Network

Post Operative Management in Intensive Care of Obstructive Jaundice caused by Choledocholithiasis: A Case Report

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