The F2RaD Score: A Novel Prediction Score and Calculator Tool to Identify Patients at Risk of Postoperative C5 Palsy

2020 ◽  
Vol 19 (5) ◽  
pp. 582-588 ◽  
Author(s):  
Daniel Lubelski ◽  
Zach Pennington ◽  
James Feghali ◽  
Andrew Schilling ◽  
Jeff Ehresman ◽  
...  
Keyword(s):  
Scoring System ◽  
Operating Characteristic ◽  
Multivariable Analysis ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Web Based ◽  
C5 Palsy ◽  
Predicted Probability ◽  
Patients At Risk ◽  
Risk Patients

Abstract BACKGROUND Postoperative C5 palsy is a debilitating complication following posterior cervical decompression. OBJECTIVE To create a simple clinical risk score predicting the occurrence of C5 palsy METHODS We retrospectively reviewed all patients who underwent posterior cervical decompressions between 2007 and 2017. Data was randomly split into training and validation datasets. Multivariable analysis was performed to construct the model from the training dataset. A scoring system was developed based on the model coefficients and a web-based calculator was deployed. RESULTS The cohort consisted of 415 patients, of which 65 (16%) developed C5 palsy. The optimal model consisted of: mean C4/5 foraminal diameter (odds ratio [OR] = 9.1 for lowest quartile compared to highest quartile), preoperative C5 radiculopathy (OR = 3.5), and dexterity loss (OR = 2.9). The receiver operating characteristic yielded an area under the curve of 0.757 and 0.706 in the training and validation datasets, respectively. Every characteristic was worth 1 point except the lowest quartile of mean C4/5 foraminal diameter, which was worth 2 points, and the factors were summarized by the acronym F2RaD. The median predicted probability of C5 palsy increased from 2% in patients with a score of 0 to 70% in patients with a score of 4. The calculator can be accessed on https://jhuspine2.shinyapps.io/FRADscore/. CONCLUSION This study yielded a simplified scoring system and clinical calculator that predicts the occurrence of C5 palsy. Individualized risk prediction for patients may facilitate better understanding of the risks and benefits for an operation, and better prepare them for this possible adverse outcome. Furthermore, modifying the surgical plan in high-risk patients may possibly improve outcomes.

scholarly journals Predicting Persistent Opioid Use, Abuse, and Toxicity Among Cancer Survivors

2019 ◽  
Vol 112 (7) ◽  
pp. 720-727 ◽  
Author(s):  
Lucas K Vitzthum ◽  
Paul Riviere ◽  
Paige Sheridan ◽  
Vinit Nalawade ◽  
Rishi Deka ◽  
...  
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Cancer Survivors ◽  
Critical Role ◽  
Multivariable Analysis ◽  
Area Under The Curve ◽  
Opioid Abuse ◽  
Opioid Use ◽  
Multivariable Logistic Regression Model ◽  
Patients At Risk

Abstract Background Although opioids play a critical role in the management of cancer pain, the ongoing opioid epidemic has raised concerns regarding their persistent use and abuse. We lack data-driven tools in oncology to understand the risk of adverse opioid-related outcomes. This project seeks to identify clinical risk factors and create a risk score to help identify patients at risk of persistent opioid use and abuse. Methods Within a cohort of 106 732 military veteran cancer survivors diagnosed between 2000 and 2015, we determined rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity. A multivariable logistic regression model was used to identify patient, cancer, and treatment risk factors associated with adverse opioid-related outcomes. Predictive risk models were developed and validated using a least absolute shrinkage and selection operator regression technique. Results The rate of persistent opioid use in cancer survivors was 8.3% (95% CI = 8.1% to 8.4%); the rate of opioid abuse or dependence was 2.9% (95% CI = 2.8% to 3.0%); and the rate of opioid-related admissions was 2.1% (95% CI = 2.0% to 2.2%). On multivariable analysis, several patient, demographic, and cancer and treatment factors were associated with risk of persistent opioid use. Predictive models showed a high level of discrimination when identifying individuals at risk of adverse opioid-related outcomes including persistent opioid use (area under the curve [AUC] = 0.85), future diagnoses of opioid abuse or dependence (AUC = 0.87), and admission for opioid abuse or toxicity (AUC = 0.78). Conclusion This study demonstrates the potential to predict adverse opioid-related outcomes among cancer survivors. With further validation, personalized risk-stratification approaches could guide management when prescribing opioids in cancer patients.

A novel risk score for prediction of hypersensitivity reactions in cancer patients receiving carboplatin: Retrospective observational analysis.

2016 ◽  
Vol 34 (3_suppl) ◽  
pp. e282-e282
Author(s):  
Orawan Suppramote ◽  
Prapatsara Pongpunpisand ◽  
Kanlaya Ladkam ◽  
Somkiat Rujirawat
Keyword(s):  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Scoring System ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Cancer Type ◽  
Hypersensitivity Reactions ◽  
Risk Patients ◽  
Carboplatin Hypersensitivity

e282 Background: Hypersentitivity reactions (HSRs) from carboplatin are high incidence and most severity in Chulabhorn hospital. These reactions are associated with several causes including patient factors and experience in drug used. A reliable and valid tool for evaluated risk of HSRs before started carboplatin infusion should lead to prevent or decrease severity of the reactions. We innovated risk score to screen patient at high risk of HSRs. Methods: From October 2013 to September 2014, all cancer patients who received carboplatin in Chulabhorn hospital were included. A retrospective study design to developed risk scoring system for prediction of patients at high risk of carboplatin hypersensitivity called “Hypersensitivity risk score”. The hypersensitivity risk score was calculated for all patients receiving carboplatin and data for carboplatin hypersensitivity were obtained from medical records. Expected and observed HSRs were analyzed by using receiver operating characteristic (ROC) curve. Results: Seventy-three cancer patients received carboplatin and five (7%) patients had HSRs. Our scoring algorithm based on cancer type, number of carboplatin retreatment, duration between each retreatment, and number of carboplatin infusions prior to first reaction. All significant predictors were weighted into points and categorized to risk group which ranged from 0 to 8 . The ROC analysis for hypersensitivity risk score indicated good predictive accuracy with an area under the curve of 0.96 (95 %CI: 0.91-1.00). Data showed high sensitivity (80%) and specificity (94.85%) for a risk score cut-off of 4. The hypersensitivity risk score clearly differentiated the low (0-1), intermediate (2-3) and intermediate-high (4-5) and high (6-8) risk patients. Conclusions: The hypersensitivity risk score is a simple scoring system with high predictive value and differentiates low versus high risk patients. This score should be used for screen high risk of hypersensitivity reactions in patients receiving carboplatin.

scholarly journals Development of risk factor-based scoring system for detection of hypervirulent Klebsiella pneumoniae bloodstream infections.

2020 ◽  
Author(s):  
Atsushi Togawa ◽  
Michinobu Yoshimura ◽  
Chiemi Tokushige ◽  
Akira Matsunaga ◽  
Tohru Takata ◽  
...  
Keyword(s):  
Risk Factors ◽  
Klebsiella Pneumoniae ◽  
Scoring System ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Clinical Manifestations ◽  
Bloodstream Infections ◽  
Patient Demographics ◽  
Patients At Risk ◽  
Community Onset

Abstract Background Hypervirulent Klebsiella pneumoniae (HVKp) infections have distinct clinical manifestations from classical K. pneumoniae infections. The hallmark of HVKp infections are liver abscess formation and metastatic infections. Due to the severe sequelae of these complications, method to identify patients at-risk of HVKp infections should be developed. Results A retrospective cohort study of 222 patients with K. pneumoniae bloodstream infections (BSIs) was performed. Patient demographics, clinical manifestations, and bacterial characteristics were investigated. Ten cases of liver abscesses were identified. Characteristics such as community-onset BSIs, hypermucoviscosity phenotype, and capsular serotype K1 were identified as risk factors for HVKp infections. A scoring system was developed based on the risk factors. The area under the receiver operating characteristic curve for the scoring system was 0.90. A score of >2 points provided sensitivity and specificity of 0.70 and 0.94, respectively. Conclusions Simple scoring system was developed for the diagnosis of HVKp infections. The system allows early identification of patients with K. pneumoniae BSIs in whom hypervirulent infections should be evaluated. Prospective evaluation is expected.

scholarly journals Development of risk factor-based scoring system for detection of hypervirulent Klebsiella pneumoniae bloodstream infections.

2020 ◽  
Author(s):  
Atsushi Togawa ◽  
Michinobu Yoshimura ◽  
Chiemi Tokushige ◽  
Akira Matsunaga ◽  
Tohru Takata ◽  
...  
Keyword(s):  
Risk Factors ◽  
Klebsiella Pneumoniae ◽  
Scoring System ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Clinical Manifestations ◽  
Bloodstream Infections ◽  
Patient Demographics ◽  
Patients At Risk ◽  
Community Onset

Abstract Background Hypervirulent Klebsiella pneumoniae (HVKp) infections have distinct clinical manifestations from classical K. pneumoniae infections. The hallmark of HVKp infections are liver abscess formation and metastatic infections. Due to the severe sequelae of these complications, method to identify patients at-risk of HVKp infections should be developed. Results A retrospective cohort study of 222 patients with K. pneumoniae bloodstream infections (BSIs) was performed. Patient demographics, clinical manifestations, and bacterial characteristics were investigated. Ten cases of liver abscesses were identified. Characteristics such as community-onset BSIs, hypermucoviscosity phenotype, and capsular serotype K1 were identified as risk factors for HVKp infections. A scoring system was developed based on the risk factors. The area under the receiver operating characteristic curve for the scoring system was 0.90. A score of ≥ 2 points provided sensitivity and specificity of 0.70 and 0.94, respectively. Conclusions Simple scoring system was developed for the diagnosis of HVKp infections. The system allows early identification of patients with K. pneumoniae BSIs in whom hypervirulent infections should be evaluated. Prospective evaluation is expected.

scholarly journals Predictive Nomogram for Early Recurrence after Pancreatectomy in Resectable Pancreatic Cancer: Risk Classification Using Preoperative Clinicopathologic Factors

Cancers ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 137
Author(s):  
Naru Kim ◽  
In Woong Han ◽  
Youngju Ryu ◽  
Dae Wook Hwang ◽  
Jin Seok Heo ◽  
...  
Keyword(s):  
Medical Center ◽  
Multivariable Analysis ◽  
Area Under The Curve ◽  
Early Recurrence ◽  
Ductal Adenocarcinoma ◽  
P Value ◽  
Resectable Pancreatic Cancer ◽  
Web Based ◽  
Factors Affecting ◽  
Clinicopathologic Factors

The survival of patients with pancreatic ductal adenocarcinoma (PDAC) is closely related to recurrence. It is necessary to classify the risk factors for early recurrence and to develop a tool for predicting the initial outcome after surgery. Among patients with resected resectable PDAC at Samsung Medical Center (Seoul, Korea) between January 2007 and December 2016, 631 patients were classified as the training set. Analyses identifying preoperative factors affecting early recurrence after surgery were performed. When the p-value estimated from univariable Cox’s proportional hazard regression analysis was <0.05, the variables were included in multivariable analysis and used for establishing the nomogram. The established nomogram predicted the probability of early recurrence within 12 months after surgery in resectable PDAC. One thousand bootstrap resamplings were used to validate the nomogram. The concordance index was 0.665 (95% confidence interval [CI], 0.637–0.695), and the incremental area under the curve was 0.655 (95% CI, 0.631–0.682). We developed a web-based calculator, and the nomogram is freely available at http://pdac.smchbp.org/. This is the first nomogram to predict early recurrence after surgery for resectable PDAC in the preoperative setting, providing a method to allow proceeding to treatment customized according to the risk of individual patients.

scholarly journals A semi-automated measurement technique for the assessment of radiolucency

2014 ◽  
Vol 11 (96) ◽  
pp. 20140303 ◽  
Author(s):  
E. C. Pegg ◽  
B. J. L. Kendrick ◽  
H. G. Pandit ◽  
H. S. Gill ◽  
D. W. Murray
Keyword(s):  
Quantitative Assessment ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Imaging Algorithm ◽  
Receiver Operating Characteristic Curves ◽  
Automated Method ◽  
Automated Imaging ◽  
Kappa Value ◽  
Patients At Risk ◽  
Total Agreement

The assessment of radiolucency around an implant is qualitative, poorly defined and has low agreement between clinicians. Accurate and repeatable assessment of radiolucency is essential to prevent misdiagnosis, minimize cases of unnecessary revision, and to correctly monitor and treat patients at risk of loosening and implant failure. The purpose of this study was to examine whether a semi-automated imaging algorithm could improve repeatability and enable quantitative assessment of radiolucency. Six surgeons assessed 38 radiographs of knees after unicompartmental knee arthroplasty for radiolucency, and results were compared with assessments made by the semi-automated program. Large variation was found between the surgeon results, with total agreement in only 9.4% of zones and a kappa value of 0.602; whereas the automated program had total agreement in 81.6% of zones and a kappa value of 0.802. The software had a ‘fair to excellent’ prediction of the presence or the absence of radiolucency, where the area under the curve of the receiver operating characteristic curves was 0.82 on average. The software predicted radiolucency equally well for cemented and cementless implants ( p = 0.996). The identification of radiolucency using an automated method is feasible and these results indicate that it could aid the definition and quantification of radiolucency.

scholarly journals The LACE Score as a Tool to Identify Radical Cystectomy Patients at Increased Risk of 90-Day Readmission and Mortality

2018 ◽  
Vol 12 (1) ◽  
pp. 20-26 ◽  
Author(s):  
Jennifer L. Saluk ◽  
Robert H. Blackwell ◽  
William S. Gange ◽  
Matthew A. C. Zapf ◽  
Anai N. Kothari ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Radical Cystectomy ◽  
Multivariable Analysis ◽  
Emergency Department Visits ◽  
High Risk Patients ◽  
Increased Risk ◽  
Patients At Risk ◽  
Risk Patients

Introduction: Radical cystectomy for bladder cancer is associated with high rates of readmission. We investigated the LACE score, a validated prediction tool for readmission and mortality, in the radical cystectomy population. Materials &amp; Methods: Patients who underwent radical cystectomy for bladder cancer were identified by ICD-9 codes from the Healthcare Cost and Utilization Project State Inpatient Database for California years 2007-2010. The LACE score was calculated as previously described, with components of L: length of stay, A: acuity of admission, C: comorbidity, and E: number of emergency department visits within 6 months preceding surgery. Results: Of 3,470 radical cystectomy patients, 638 (18.4%) experienced 90-day readmission, and 160 (4.6%) 90-day mortality. At a previously validated “high-risk” LACE score ≥ 10, patients experienced an increased risk of 90-day readmission (22.8 vs. 17.7%, p = 0.002) and mortality (9.1 vs. 3.5%, p < 0.001). On adjusted multivariable analysis, “high risk” patients by LACE score had increased 90-day odds of readmission (adjusted OR = 1.24, 95% CI: 0.99-1.54, p = 0.050) and mortality (adjusted OR = 2.09, 95% CI: 1.47-2.99, p < 0.001). Conclusion: The LACE score reasonably identifies patients at risk for 90-day mortality following radical cystectomy, but only poorly predicts readmission. Providers may use the LACE score to target high-risk patients for closer follow-up or intervention.

scholarly journals Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

10.2196/14993 ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. e14993
Author(s):  
Hani Nabeel Mufti ◽  
Gregory Marshal Hirsch ◽  
Samina Raza Abidi ◽  
Syed Sibte Raza Abidi
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Postoperative Delirium ◽  
Class Imbalance ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Learning Methods ◽  
Test Dataset ◽  
Machine Learning Methods ◽  
Patients At Risk

Background Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. Objective This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. Methods We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees. Results Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm’s performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). Conclusions Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model’s performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients’ outcomes.

scholarly journals Prediction of patients requiring intensive care for COVID-19: development and validation of an integer-based score using data from Centers for Disease Control and Prevention of South Korea.

2020 ◽  
Author(s):  
JoonNyung Heo ◽  
Deokjae Han ◽  
Hyung-Jun Kim ◽  
Daehyun Kim ◽  
Yeon-Kyeng Lee ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
South Korea ◽  
Disease Control ◽  
Scoring System ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Control And Prevention ◽  
Laboratory Variables

Abstract Background Unavailability or saturation of the intensive care unit may be associated with the fatality of COVID-19. Prioritizing the patients for hospitalization and intensive care may be critical for reducing the fatality of COVID-19. This study aimed to develop and validate a new integer-based scoring system for predicting patients with COVID-19 requiring intensive care, using only the predictors available upon triage. Methods This is a retrospective study using cohort data from the Korean Centers for Disease Control and Prevention that included all admitted patients with COVID-19 between January 19 and June 3, 2020, in South Korea. The primary outcome was patients requiring intensive care defined as actual admission to the intensive care unit; at any time use of an extracorporeal life support device, mechanical ventilation, or vasopressors; death. Patients admitted until March 20 were included for the training dataset to develop the prediction models and externally validated for the patients admitted afterward. Two logistic regression models were developed with different predictors and the predictive performance was compared: one with patient-provided variables and the other with added radiologic and laboratory variables. An integer-based scoring system was developed based on the developed logistic regression model.Results A total of 5,193 patients were considered, with 4,663 patients included after excluding patients with age under 18 or insufficient data. For the training dataset, 3,238 patients were included. Of the included patients, 444 (9.5%) patients required intensive care. The model developed with only the clinical variables showed an area under the curve of 0.884 for the validation set. The performance did not differ when radiologic and laboratory variables were added. Seven variables were selected for developing an integer-based scoring system: age, sex, initial body temperature, dyspnea, hemoptysis, history of chronic kidney disease, and activities of daily living. The area under the curve of the scoring system was 0.880. Conclusions An integer-based scoring system was developed for predicting patients with COVID-19 requiring intensive care, with high performance. This system may aid decision support for prioritizing the patient for hospitalization and intensive care, particularly in a situation with limited medical resources.

scholarly journals Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study (Preprint)

2019 ◽  
Author(s):  
Hani Nabeel Mufti ◽  
Gregory Marshal Hirsch ◽  
Samina Raza Abidi ◽  
Syed Sibte Raza Abidi
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Postoperative Delirium ◽  
Class Imbalance ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Learning Methods ◽  
Test Dataset ◽  
Machine Learning Methods ◽  
Patients At Risk

BACKGROUND Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. OBJECTIVE This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. METHODS We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees. RESULTS Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm’s performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a <italic>P</italic>=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; <italic>P</italic>=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; <italic>P</italic>=.03). CONCLUSIONS Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model’s performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients’ outcomes.

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