Predictive modeling of in-hospital mortality following elective surgery

2021 ◽  
Author(s):  
Michael P. Rogers ◽  
Anthony J. DeSantis ◽  
Paul C. Kuo ◽  
Haroon M. Janjua
Keyword(s):  
Hospital Mortality ◽  
Predictive Modeling ◽  
Elective Surgery

scholarly journals Preoperative Risk Assessment: A Poor Predictor of Outcome in Critically ill Elderly with Sepsis After Abdominal Surgery

2020 ◽  
Vol 44 (12) ◽  
pp. 4060-4069
Author(s):  
Anne C. M. Cuijpers ◽  
Marielle M. E. Coolsen ◽  
Ronny M. Schnabel ◽  
Susanne van Santen ◽  
Steven W. M. Olde Damink ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Abdominal Surgery ◽  
Hospital Mortality ◽  
Emergency Surgery ◽  
Outcome Prediction ◽  
Elective Surgery ◽  
Perioperative Risk ◽  
Physical Status ◽  
Icu Admission ◽  
Apache Iv

Abstract Background Postoperative outcome prediction in elderly is based on preoperative physical status but its predictive value is uncertain. The goal was to evaluate the value of risk assessment performed perioperatively in predicting outcome in case of admission to an intensive care unit (ICU). Methods A total of 108 postsurgical patients were retrospectively selected from a prospectively recorded database of 144 elderly septic patients (>70 years) admitted to the ICU department after elective or emergency abdominal surgery between 2012 and 2017. Perioperative risk assessment scores including Portsmouth Physiological and Operative Severity Score for the enumeration of Mortality (P-POSSUM) and American Society of Anaesthesiologists Physical Status classification (ASA) were determined. Acute Physiology and Chronic Health Evaluation IV (APACHE IV) was obtained at ICU admission. Results In-hospital mortality was 48.9% in elderly requiring ICU admission after elective surgery (n = 45), compared to 49.2% after emergency surgery (n = 63). APACHE IV significantly predicted in-hospital mortality after complicated elective surgery [area under the curve 0.935 (p < 0.001)] where outpatient ASA physical status and P-POSSUM did not. In contrast, P-POSSUM and APACHE IV significantly predicted in-hospital mortality when based on current physical state in elderly requiring emergency surgery (AUC 0.769 (p = 0.002) and 0.736 (p = 0.006), respectively). Conclusions Perioperative risk assessment reflecting premorbid physical status of elderly loses its value when complications occur requiring unplanned ICU admission. Risks in elderly should be re-assessed based on current clinical condition prior to ICU admission, because outcome prediction is more reliable then.

scholarly journals Predictors of in-hospital mortality in patients undergoing elective surgery in a university hospital: a prospective cohort

2018 ◽  
Vol 68 (5) ◽  
pp. 492-498
Author(s):  
Adriene Stahlschmidt ◽  
Betânia Novelo ◽  
Luiza Alexi Freitas ◽  
Sávio Cavalcante Passos ◽  
Jairo Alberto Dussán-Sarria ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prospective Cohort ◽  
Elective Surgery ◽  
University Hospital

scholarly journals Development of Multivariable Models to Predict and Benchmark Transfusion in Elective Surgery Supporting Patient Blood Management

2017 ◽  
Vol 08 (02) ◽  
pp. 617-631 ◽  
Author(s):  
Dieter Hayn ◽  
Karl Kreiner ◽  
Hubert Ebner ◽  
Peter Kastner ◽  
Nada Breznik ◽  
...  
Keyword(s):  
Blood Transfusion ◽  
Red Blood Cells ◽  
Predictive Modeling ◽  
Blood Cells ◽  
Elective Surgery ◽  
Patient Blood Management ◽  
Learning Tools ◽  
Blood Management ◽  
Clinical Scenarios ◽  
Multivariable Models

SummaryBackground: Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore, the concept of Patient Blood Management has increasingly gained importance to pre-empt and reduce transfusion and to identify the optimal transfusion volume for an individual patient when transfusion is indicated.Objectives: It was our aim to describe, how predictive modeling and machine learning tools applied on pre-operative data can be used to predict the amount of red blood cells to be transfused during surgery and to prospectively optimize blood ordering schedules. In addition, the data derived from the predictive models should be used to benchmark different hospitals concerning their blood transfusion patterns.Methods: 6,530 case records obtained for elective surgeries from 16 centers taking part in two studies conducted in 2004–2005 and 2009–2010 were analyzed. Transfused red blood cell volume was predicted using random forests. Separate models were trained for overall data, for each center and for each of the two studies. Important characteristics of different models were compared with one another.Results: Our results indicate that predictive modeling applied prior surgery can predict the transfused volume of red blood cells more accurately (correlation coefficient cc = 0.61) than state of the art algorithms (cc = 0.39). We found significantly different patterns of feature importance a) in different hospitals and b) between study 1 and study 2.Conclusion: We conclude that predictive modeling can be used to benchmark the importance of different features on the models derived with data from different hospitals. This might help to optimize crucial processes in a specific hospital, even in other scenarios beyond Patient Blood Management.Citation: Hayn D, Kreiner K, Ebner H, Kastner P, Breznik N, Rzepka A, Hofmann A, Gombotz H, Schreier G. Development of multivariable models to predict and benchmark transfusion in elective surgery supporting patient blood management. Appl Clin Inform 2017; 8: 617–631 https://doi.org/10.4338/ACI-2016-11-RA-0195

The SYSTEMIC Project: A Pilot Study to Develop a Registry of Patients With an Ostomy for Predictive Modeling of Outcomes

2021 ◽  
Vol 67 (4) ◽  
pp. 24-34
Author(s):  
Honoria Ocagli ◽  
Giulia Lorenzoni ◽  
Daniele Bottigliengo ◽  
Danila Azzolina ◽  
Lucia Stivanello ◽  
...  
Keyword(s):  
Predictive Modeling ◽  
Naive Bayes ◽  
Elective Surgery ◽  
Bowel Perforation ◽  
Well Being ◽  
Naïve Bayes ◽  
University Hospital ◽  
Learning Approaches ◽  
Predictive Tool ◽  
Peristomal Complications

BACKGROUND: Stomal and peristomal skin complications represent a significant burden on the physical and psychological well-being of patients. PURPOSE: To develop a predictive tool for identifying the risk of complications in patients following ostomy surgery. METHODS: The oStomY regiSTry prEdictive ModelIng outCome (SYSTEMIC) project was developed to improve patient-oriented outcomes. Demographic, medical history, and stoma-related variables were obtained from patients at the wound ostomy clinic of the University Hospital of Padova, Italy. A follow-up assessment was completed 30 days after stoma surgery. Two (2) Bayesian machine learning approaches (naïve Bayes) were carried out to define an automatic peristomal complication predictive tool. A sensitivity analysis was performed to evaluate the possible effects of the prior choices on naïve Bayes performance. RESULTS: The algorithms were based on preliminary data from 52 patients (28 [53.3%] had a colostomy and 24 [46.7%] had an ileostomy). In terms of postoperative complications, no significant differences were observed between patients with different body mass indices (P = .16), those who underwent elective surgery compared with those who underwent emergency surgery (P = .66), and those who had or had not been preoperatively sited (P = .44). The algorithms showed an overall moderate ability to correctly classify patients according to the presence of peristomal complications (accuracy of nearly 70% in both models). In the the data-driven prior model, the probability of developing complications was greater for participants with malignancies or other diseases (0.3314 for both levels) than for patients with diverticula and bowel perforation (0.1453) or inflammatory bowel disease (0.1918). CONCLUSION: The development of an easy-to-use algorithm may help nonspecialized nurses evaluate the likelihood of future peristomal complications in patients with an ostomy and implement preemptive measures.

scholarly journals P2.17-13 Thoracoscore Fails to Predict In-Hospital Mortality Following Elective Surgery in a Brazilian Lung Cancer Cohort

2019 ◽  
Vol 14 (10) ◽  
pp. S888-S889
Author(s):  
E. Ricardo ◽  
F. Abrao ◽  
F. Moreira ◽  
I. Abreu ◽  
R. Younes ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Hospital Mortality ◽  
Elective Surgery

scholarly journals Temporal Trends in the Epidemiology of Severe Postoperative Sepsis after Elective Surgery

2010 ◽  
Vol 112 (4) ◽  
pp. 917-925 ◽  
Author(s):  
Brian T. Bateman ◽  
Ulrich Schmidt ◽  
Mitchell F. Berman ◽  
Edward A. Bittner
Keyword(s):  
Severe Sepsis ◽  
Mortality Rate ◽  
Hospital Mortality ◽  
Odds Ratio ◽  
Adjusted Odds Ratio ◽  
Elective Surgery ◽  
Operative Procedure ◽  
Temporal Trends ◽  
The United States ◽  
Postoperative Sepsis

Introduction Multiple studies have used administrative datasets to examine the epidemiology of sepsis in general, but the entity of postoperative sepsis has been studied less intensively. Therefore, we undertook an analysis of the epidemiology of postoperative sepsis using the Nationwide Inpatient Sample, the largest in-patient dataset available in the United States. Methods Elective admissions of patients aged 18 yr or older with a length of stay more than 3 days for any 1 of the 20 most common elective operative procedures were extracted from the dataset for the years 1997-2006. Postoperative sepsis was defined using the appropriate International Classification of Diseases, Ninth Revision, Clinical Modification codes; severe sepsis was defined as sepsis along with organ dysfunction. Logistic regression was used to assess the significance of temporal trends after adjusting for relevant demographic characteristics, operative procedure, and comorbid conditions. Results We identified 2,039,776 admissions for analysis. The rate of severe sepsis increased from 0.3% in 1997 to 0.9% in 2006. This trend persisted after adjusting for relevant covariables-the adjusted odds ratio of severe sepsis per year increase in the study period was 1.12 (95% CI, 1.11-1.13; P &lt; 0.001). The in-hospital mortality rate for patients with severe postoperative sepsis declined from 44.4% in 1997 to 34.0% in 2006; this trend also persisted after adjustment for relevant covariables-the adjusted odds ratio per year was 0.94 (95% CI, 0.93-0.95; P &lt; 0.001). Conclusion During the 10-yr period that we studied, there was a marked increase in the rate of severe postoperative sepsis but a concomitant decrease in the in-hospital mortality rate in severe sepsis.

The changing in-hospital mortality of alcoholic hepatitis

2001 ◽  
Vol 120 (5) ◽  
pp. A544-A544
Author(s):  
Y GUNDAMRAG ◽  
A QUADRI ◽  
N VAKIL
Keyword(s):  
Hospital Mortality ◽  
Alcoholic Hepatitis
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