scholarly journals The Cumulative Perioperative Model: Predicting 30-Day Mortality in Abdominal Surgery Cancer Patients

2020 ◽  
pp. 1-9
Author(s):  
Joseph L Nates ◽  
Christopher M Jermaine ◽  
Joseph Ruiz ◽  
Joseph L Nates ◽  
Risa B Myers
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Abdominal Surgery ◽  
Cancer Patients ◽  
Mortality Risk ◽  
Predictive Ability ◽  
Mortality Prediction ◽  
Multivariate Logistic Regression Model ◽  
Cancer Center ◽  
Prediction Ability

Objectives: 1) To develop a cumulative perioperative model (CPM) using the hospital clinical course of abdominal surgery cancer patients that predicts 30 and 90-day mortality risk; 2) To compare the predictive ability of this model to ten existing other models. Materials and Methods: We constructed a multivariate logistic regression model of 30 (90)-day mortality, which occurred in 106 (290) of the cases, using 13,877 major abdominal surgical cases performed at the University of Texas MD Anderson Cancer Center from January 2007 to March 2014. The model includes race, starting location (home, inpatient ward, intensive care unit or emergency center), Charlson Comorbidity Index, emergency status, ASA-PS classification, procedure, surgical Apgar score, destination after surgery (hospital ward location) and delayed intensive care unit admit within six days. We computed and compared the model mortality prediction ability (C-statistic) as we accumulated features over time. Results: We were able to predict 30 (90)-day mortality with C-statistics from 0.70 (0.71) initially to 0.87 (0.84) within six days postoperatively. Conclusion: We achieved a high level of model discrimination. The CPM enables a continuous cumulative assessment of the patient’s mortality risk, which could then be used as a decision support aid regarding patient care and treatment, potentially resulting in improved outcomes, decreased costs and more informed decisions.

scholarly journals Severity and Outcome Detection for the Cancer Patients with COVID-19 Using Machine Learning Models

2021 ◽  
Author(s):  
Akbar Davoodi ◽  
Shaghayegh Haghjooy Javanmard ◽  
Golnaz Vaseghi ◽  
Amirreza Manteghinejad
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Cancer Patients ◽  
Cancer Center ◽  
Support Vector ◽  
Neutrophil Lymphocyte Ratio ◽  
Icu Admission ◽  
Reactive Protein ◽  
Risk Of Death

Abstract Background:The COVID-19 pandemic challenges the healthcare system to provide enough resources to battle the pandemic without jeopardizing routine treatments. As a result, this is important that we can predict the outcomes of patients at the time of admission. This study aims to apply different machine learning (ML) models for predicting Intensive Care Unit (ICU) admission and mortality of Cancer Patients infected with COVID-19.Methods:This study's data were collected from a referral cancer center in Iran. The study included all patients with cancer and a confirmed diagnosis of COVID-19.Different ML prediction algorithms like Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbours (kNN), Random Forest (RF), and Support Vector Machine (SVM) were used. Also, we applied the SelectKBest method to find the most important features for predicting ICU admission and mortality.Results:Three hundred thirty-nine patients enrolled in the study. One hundred fifteen were admitted to the Intensive Care Unit (ICU), and 118 patients died during the hospital admission. The Area Under Curve (AUC) for predicting mortality is 0.61 for LR, 0.74 for NB, 0.61 for kNN, 0.6 for SVM, and 0.79 for RF. The AUC for predicting ICU admission is 0.61 for LR, 0.74 for NB, 0.56 for kNN, 0.55 for SVM, and 0.7 for RF.C-reactive protein (CRP), Aspartate transaminase (AST), and Neutrophil-Lymphocyte Ratio (NLR) also are the most common features in predicting ICU admission and mortality.Conclusion:Our findings show the promise of different AI methods for predicting the risk of death or ICU in cancer patients infected with COVID-19, highlighting the importance of first laboratory results and patients' symptoms.

scholarly journals A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ding Li ◽  
Tianjiao Li ◽  
Changsen Bai ◽  
Qing Zhang ◽  
Zheng Li ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Invasive Candidiasis ◽  
Multivariate Logistic Regression Analysis ◽  
Bloodstream Infections ◽  
Mortality Prediction ◽  
Source Control ◽  
Cancer Center ◽  
Net Benefit ◽  
Opt Out ◽  
Risk Patients

Abstract Background Invasive candidiasis is the most common fungal disease among hospitalized patients and continues to be a major cause of mortality. Risk factors for mortality have been studied previously but rarely developed into a predictive nomogram, especially for cancer patients. We constructed a nomogram for mortality prediction based on a retrospective review of 10 years of data for cancer patients with invasive candidiasis. Methods Clinical data for cancer patients with invasive candidiasis during the period of 2010–2019 were studied; the cases were randomly divided into training and validation cohorts. Variables in the training cohort were subjected to a predictive nomogram based on multivariate logistic regression analysis and a stepwise algorithm. We assessed the performance of the nomogram through the area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA) in both the training and validation cohorts. Results A total of 207 cases of invasive candidiasis were examined, and the crude 30-day mortality was 28.0%. Candida albicans (48.3%) was the predominant species responsible for infection, followed by the Candida glabrata complex (24.2%) and Candida tropicalis (10.1%). The training and validation cohorts contained 147 and 60 cases, respectively. The predictive nomogram consisted of bloodstream infections, intensive care unit (ICU) admitted > 3 days, no prior surgery, metastasis and no source control. The AUCs of the training and validation cohorts were 0.895 (95% confidence interval [CI], 0.846–0.945) and 0.862 (95% CI, 0.770–0.955), respectively. The net benefit of the model performed better than “treatment for all” in DCA and was also better for opting low-risk patients out of treatment than “treatment for none” in opt-out DCA. Conclusion Cancer patients with invasive candidiasis exhibit high crude mortality. The predictive nomogram established in this study can provide a probability of mortality for a given patient, which will be beneficial for therapeutic strategies and outcome improvement.

scholarly journals 536P Characteristics and outcome of cancer patients with sepsis requiring admission to the intensive care unit

2016 ◽  
Vol 27 ◽  
pp. ix174
Author(s):  
A.J. Sunggoro ◽  
A. Arifin ◽  
S. Marwanta ◽  
S.M. Atmodjo ◽  
S. Maryono
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Cancer Patients

P-168 Outcome of lung cancer patients admitted to intensive care unit

Lung Cancer ◽  
2005 ◽  
Vol 49 ◽  
pp. S159
Author(s):  
J. Maniate ◽  
S. Sharma ◽  
S. Navaratnam
Keyword(s):  
Lung Cancer ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Cancer Patients ◽  
Lung Cancer Patients
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