scholarly journals Palliative Gastrointestinal Surgery in Patients With Advanced Peritoneal Carcinomatosis: Clinical Experience and Development of a Predictive Model for Surgical Outcomes

2022 ◽  
Vol 11 ◽  
Author(s):  
Jolene Si Min Wong ◽  
Sze Min Lek ◽  
Daniel Yan Zheng Lim ◽  
Claramae Shulyn Chia ◽  
Grace Hwei Ching Tan ◽  
...  

BackgroundPalliative gastrointestinal (GI) surgery potentially relieves distressing symptoms arising from intestinal obstruction (IO) in patients with advanced peritoneal carcinomatosis (PC). As surgery is associated with significant morbidity risks in advanced cancer patients, it is important for surgeons to select patients who can benefit the most from this approach. Hence, we aim to determine predictors of morbidity and mortality after palliative surgery in patients with PC. In addition, we evaluate the utility of the UC Davis Cancer Care nomogram (UCDCCn) and develop a simplified model to predict short-term surgical mortality in these patients.MethodsA retrospective review of patients with IO secondary to PC undergoing palliative GI surgery was performed. Logistic regression was used to determine independent predictors of 30-day morbidity and mortality after surgery. UCDCCn was evaluated using the area under the curve (AUC) for discriminatory power and the Hosmer-Lemeshow test for calibration. Our simplified model was developed using logistic regression and evaluated using cross-validation.ResultsA total of 254 palliative GI surgeries were performed over a 10-year duration. The 30-day morbidity and mortality were 43% (n = 110) and 21% (n = 53), respectively. Preoperative albumin, age, and emergency nature of surgery were significant independent predictors for 30-day morbidity. A simplified model using preoperative Eastern Cooperative Oncology Group (ECOG) status and albumin (AUC = 0.71) achieved better predictive power than UCDCCn (AUC = 0.66) for 30-day mortality.ConclusionGood ECOG status and high preoperative albumin levels were independently associated with good short-term outcomes after palliative GI surgery. Our simplified model may be used to conveniently and efficiently select patients who stand to benefit the most from surgery.

2021 ◽  
Vol 8 ◽  
Author(s):  
Qiong Xue ◽  
Duan Wen ◽  
Mu-Huo Ji ◽  
Jianhua Tong ◽  
Jian-Jun Yang ◽  
...  

Objective: Investigate whether machine learning can predict pulmonary complications (PPCs) after emergency gastrointestinal surgery in patients with acute diffuse peritonitis.Methods: This is a secondary data analysis study. We use five machine learning algorithms (Logistic regression, DecisionTree, GradientBoosting, Xgbc, and gbm) to predict postoperative pulmonary complications.Results: Nine hundred and twenty-six cases were included in this study; 187 cases (20.19%) had PPCs. The five most important variables for the postoperative weight were preoperative albumin, cholesterol on the 3rd day after surgery, albumin on the day of surgery, platelet count on the 1st day after surgery and cholesterol count on the 1st day after surgery for pulmonary complications. In the test group: the logistic regression model shows AUC = 0.808, accuracy = 0.824 and precision = 0.621; Decision tree shows AUC = 0.702, accuracy = 0.795 and precision = 0.486; The GradientBoosting model shows AUC = 0.788, accuracy = 0.827 and precision = 1.000; The Xgbc model shows AUC = 0.784, accuracy = 0.806 and precision = 0.583. The Gbm model shows AUC = 0.814, accuracy = 0.806 and precision = 0.750.Conclusion: Machine learning algorithms can predict patients' PPCs with acute diffuse peritonitis. Moreover, the results of the importance matrix for the Gbdt algorithm model show that albumin, cholesterol, age, and platelets are the main variables that account for the highest pulmonary complication weights.


2020 ◽  
Vol 26 (40) ◽  
pp. 5213-5219
Author(s):  
Yun Chen ◽  
Jinwei Zheng ◽  
Junping Chen

Background: Postoperative delirium (POD) is a very common complication in elderly patients with gastric cancer (GC) and associated with poor prognosis. MicroRNAs (miRNAs) serve as key post-transcriptional regulators of gene expression via targeting mRNAs and play important roles in the nervous system. This study aimed to investigate the potential predictive role of miRNAs for POD. Methods: Elderly GC patients who were scheduled to undergo elective curative resection were consequently enrolled in this study. POD was assessed at 1 day before surgery and 1-7 days after surgery following the guidance of the 5th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM V, 2013). The demographics, clinicopathologic characteristics and preoperative circulating miRNAs by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) were compared between patients with or without POD. Risk factors for POD were assessed via univariate and multivariate logistic regression analyses. Results: A total of 370 participants were enrolled, of which 63 had suffered from POD within postoperative 7 days with an incidence of 17.0%. Preoperative miR-210 was a predictor for POD with an area under the curve (AUC) of 0.921, a cut-off value of 1.67, a sensitivity of 95.11%, and a specificity of 92.06%, (P<0.001). In the multivariate logistic regression model, the relative expression of serum miR-210 was an independent risk factor for POD (OR: 3.37, 95%CI: 1.98–5.87, P=0.003). Conclusions: In conclusion, the present study highlighted that preoperative miR-210 could serve as a potential predictor for POD in elderly GC patients undergoing curative resection.


2021 ◽  
pp. 000313482110241
Author(s):  
Christine Tung ◽  
Junko Ozao-Choy ◽  
Dennis Y. Kim ◽  
Christian de Virgilio ◽  
Ashkan Moazzez

There are limited studies regarding outcomes of replacing an infected mesh with another mesh. We reviewed short-term outcomes following infected mesh removal and whether placement of new mesh is associated with worse outcomes. Patients who underwent hernia repair with infected mesh removal were identified from 2005 to 2018 American College of Surgeons-National Surgical Quality Improvement Program database. They were divided into new mesh (Mesh+) or no mesh (Mesh-) groups. Bivariate and multivariate logistic regression analyses were used to compare morbidity between the two groups and to identify associated risk factors. Of 1660 patients, 49.3% received new mesh, with higher morbidity in the Mesh+ (35.9% vs. 30.3%; P = .016), but without higher rates of surgical site infection (SSI) (21.3% vs. 19.7%; P = .465). Mesh+ had higher rates of acute kidney injury (1.3% vs. .4%; P = .028), UTI (3.1% vs. 1.3%, P = .014), ventilator dependence (4.9% vs. 2.4%; P = .006), and longer LOS (8.6 vs. 7 days, P < .001). Multivariate logistic regression showed new mesh placement (OR: 1.41; 95% CI: 1.07-1.85; P = .014), body mass index (OR: 1.02; 95% CI: 1.00-1.03; P = .022), and smoking (OR: 1.43; 95% CI: 1.05-1.95; P = .025) as risk factors independently associated with increased morbidity. New mesh placement at time of infected mesh removal is associated with increased morbidity but not with SSI. Body mass index and smoking history continue to contribute to postoperative morbidity during subsequent operations for complications.


2021 ◽  
pp. 089719002110272
Author(s):  
Joanne Huang ◽  
Jeannie D. Chan ◽  
Thu Nguyen ◽  
Rupali Jain ◽  
Zahra Kassamali Escobar

Universal area-under-the-curve (AUC) guided vancomycin therapeutic drug monitoring (TDM) is resource-intensive, cost-prohibitive, and presents a paradigm shift that leaves institutions with the quandary of defining the preferred and most practical method for TDM. We report a step-by-step quality improvement process using 4 plan-do-study-act (PDSA) cycles to provide a framework for development of a hybrid model of trough and AUC-based vancomycin monitoring. We found trough-based monitoring a pragmatic strategy as a first-tier approach when anticipated use is short-term. AUC-guided monitoring was most impactful and cost-effective when reserved for patients with high-risk for nephrotoxicity. We encourage others to consider quality improvement tools to locally adopt AUC-based monitoring.


2020 ◽  
Vol 08 (10) ◽  
pp. E1487-E1494
Author(s):  
Veeravich Jaruvongvanich ◽  
FNU Chesta ◽  
Anushka Baruah ◽  
Meher Oberoi ◽  
Daniel Adamo ◽  
...  

Abstract Background and study aims Management of malignant gastrointestinal obstruction (MGIO) is more challenging in the presence of peritoneal carcinomatosis (PC). Outcomes data to guide the management of MGIO with PC are lacking. We aimed to compare the clinical outcomes and adverse events between endoscopic and surgical palliation and identify predictors of stent success in patients with MGIO with PC. Patients and methods Consecutive inpatients with MGIO with PC between 2000 and 2018 who underwent palliative surgery or enteral stenting were included. Clinical success was defined as relief of obstructive symptoms. Results Fifty-seven patients with enteral stenting and 40 with palliative surgery were compared. The two groups did not differ in rates of technical success, 30-day mortality, or recurrence. Clinical success from a single intervention (63.2 % versus 95 %), luminal patency duration (27 days vs. 145 days), and survival length (148 days vs. 336 days) favored palliative surgery (all P < 0.05) but the patients in the surgery group had a trend toward better Eastern Cooperative Oncology Group (ECOG) status. The rate of adverse events (AEs) (10.5 % vs. 50 %), the severity of AEs, and length of hospital stay (4.5 days vs. 9 days) favored enteral stenting (P < 0.05). The need for more than one stent was associated with a higher likelihood of stent failure. Conclusions Our study suggests that enteral stenting is safer and associated with a shorter hospital stay than palliative surgery, although unlike other MGIOs, clinical success is lower in MGIO with PC. Identification of the right candidates and potential predictors of clinical success in ECOG-matched large-scale studies is needed to validate these results.


2021 ◽  
pp. 1-10
Author(s):  
I. Krug ◽  
J. Linardon ◽  
C. Greenwood ◽  
G. Youssef ◽  
J. Treasure ◽  
...  

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.


2017 ◽  
Vol 79 (02) ◽  
pp. 123-130 ◽  
Author(s):  
Whitney Muhlestein ◽  
Dallin Akagi ◽  
Justiss Kallos ◽  
Peter Morone ◽  
Kyle Weaver ◽  
...  

Objective Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.


2007 ◽  
Vol 0 (0) ◽  
pp. 070806210014002-???
Author(s):  
Jaime Aguero ◽  
Luis Almenar ◽  
Luis Martínez-Dolz ◽  
Jose A Moro ◽  
Joaquin Rueda ◽  
...  

Heart Asia ◽  
2018 ◽  
Vol 10 (2) ◽  
pp. e011069 ◽  
Author(s):  
Nicholas Gregory Ross Bayfield ◽  
Adrian Pannekoek ◽  
David Hao Tian

Currently, the choice of whether or not to electively operate on current smokers is varied among cardiothoracic surgeons. This meta-analysis aims to determine whether preoperative current versus ex-smoking status is related to short-term postoperative morbidity and mortality in cardiac surgical patients. Systematic literature searches of the PubMed, MEDLINE and Cochrane databases were carried out to identify all studies in cardiac surgery that investigated the relationship between smoking status and postoperative outcomes. Extracted data were analysed by random effects models. Primary outcomes included 30-day or in-hospital all-cause mortality and pulmonary morbidity. Overall, 13 relevant studies were identified, with 34 230 patients in current or ex-smoking subgroups. There was no difference in mortality (p=0.93). Current smokers had significantly higher risk of overall pulmonary complications (OR 1.44; 95% CI 1.27 to 1.64; p<0.001) and postoperative pneumonia (OR 1.62; 95%  CI 1.27 to 2.06; p<0.001) as well as lower risk of postoperative renal complications (OR 0.82; 95%  CI 0.70 to 0.96; p=0.01) compared with ex-smokers. There was a trend towards an increased risk of postoperative MI (OR 1.29; 95%  CI 0.95 to 1.75; p=0.10). No difference in postoperative neurological complications (p=0.15), postoperative sternal surgical site infections (p=0.20) or postoperative length of intensive care unit stay (p=0.86) was seen. Cardiac surgical patients who are current smokers at the time of operation do not have an increased 30-day mortality risk compared with ex-smokers, although they are at significantly increased risk of postoperative pulmonary complications.


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