The Hierarchical Assessment of Balance and Mobility (HABAM): a useful tool to estimate length of hospital stay following hip and knee replacement

2020 ◽  
Vol 27 (5) ◽  
pp. 1-9
Author(s):  
Jack Martin ◽  
Karen Barker
Keyword(s):  
Length Of Stay ◽  
Hospital Stay ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Length Of Hospital Stay ◽  
Patients At Risk ◽  
Operating Characteristic Curve ◽  
Hierarchical Assessment

Background/Aims The Hierarchical Assessment of Balance and Mobility is a measure of balance and mobility that can detect recovery of physical function, and can be used to identify patients at risk of delayed discharge. The aim of this study was to investigate the use of Hierarchical Assessment of Balance and Mobility scores as a predictor of length of hospital stay in patients following hip and knee replacement. Methods Hierarchical Assessment of Balance and Mobility scores were collected on 191 patients following primary total joint replacement. Regression and receiver operating characteristic curve analyses were conducted to assess the relationship between Hierarchical Assessment of Balance and Mobility and length of stay. Results Hierarchical Assessment of Balance and Mobility scores of <31 on the first post-operative day predicted longer than mean length of stay (4 days) with sensitivity and specificity of 79% and 83%. Receiver operating characteristic curve analyses showed that a Hierarchical Assessment of Balance and Mobility score of 50 was the optimal cut-off point for discharge. Conclusions Hierarchical Assessment of Balance and Mobility offers a practical way to quantify and objectively track patients' physical function, and can help identify patients at risk of an increased length of stay on post-operative day one.

scholarly journals Predicting Risk of Postoperative Lung Injury in High-risk Surgical Patients

2014 ◽  
Vol 120 (5) ◽  
pp. 1168-1181 ◽  
Author(s):  
Daryl J. Kor ◽  
Ravi K. Lingineni ◽  
Ognjen Gajic ◽  
Pauline K. Park ◽  
James M. Blum ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Surgical Patients ◽  
Patients At Risk ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract Background: Acute respiratory distress syndrome (ARDS) remains a serious postoperative complication. Although ARDS prevention is a priority, the inability to identify patients at risk for ARDS remains a barrier to progress. The authors tested and refined the previously reported surgical lung injury prediction (SLIP) model in a multicenter cohort of at-risk surgical patients. Methods: This is a secondary analysis of a multicenter, prospective cohort investigation evaluating high-risk patients undergoing surgery. Preoperative ARDS risk factors and risk modifiers were evaluated for inclusion in a parsimonious risk-prediction model. Multiple imputation and domain analysis were used to facilitate development of a refined model, designated SLIP-2. Area under the receiver operating characteristic curve and the Hosmer–Lemeshow goodness-of-fit test were used to assess model performance. Results: Among 1,562 at-risk patients, ARDS developed in 117 (7.5%). Nine independent predictors of ARDS were identified: sepsis, high-risk aortic vascular surgery, high-risk cardiac surgery, emergency surgery, cirrhosis, admission location other than home, increased respiratory rate (20 to 29 and ≥30 breaths/min), Fio2 greater than 35%, and Spo2 less than 95%. The original SLIP score performed poorly in this heterogeneous cohort with baseline risk factors for ARDS (area under the receiver operating characteristic curve [95% CI], 0.56 [0.50 to 0.62]). In contrast, SLIP-2 score performed well (area under the receiver operating characteristic curve [95% CI], 0.84 [0.81 to 0.88]). Internal validation indicated similar discrimination, with an area under the receiver operating characteristic curve of 0.84. Conclusions: In this multicenter cohort of patients at risk for ARDS, the SLIP-2 score outperformed the original SLIP score. If validated in an independent sample, this tool may help identify surgical patients at high risk for ARDS.

scholarly journals Prediction of Nephrotoxicity Associated With Cisplatin-Based Chemotherapy in Testicular Cancer Patients

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Sara L Garcia ◽  
Jakob Lauritsen ◽  
Zeyu Zhang ◽  
Mikkel Bandak ◽  
Marlene D Dalgaard ◽  
...  
Keyword(s):  
Random Forest ◽  
Testicular Cancer ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Cancer Patients ◽  
Clinical Data ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Patients At Risk ◽  
Operating Characteristic Curve

Abstract Background Cisplatin-based chemotherapy may induce nephrotoxicity. This study presents a random forest predictive model that identifies testicular cancer patients at risk of nephrotoxicity before treatment. Methods Clinical data and DNA from saliva samples were collected for 433 patients. These were genotyped on Illumina HumanOmniExpressExome-8 v1.2 (964 193 markers). Clinical and genomics-based random forest models generated a risk score for each individual to develop nephrotoxicity defined as a 20% drop in isotopic glomerular filtration rate during chemotherapy. The area under the receiver operating characteristic curve was the primary measure to evaluate models. Sensitivity, specificity, and positive and negative predictive values were used to discuss model clinical utility. Results Of 433 patients assessed in this study, 26.8% developed nephrotoxicity after bleomycin-etoposide-cisplatin treatment. Genomic markers found to be associated with nephrotoxicity were located at NAT1, NAT2, and the intergenic region of CNTN6 and CNTN4. These, in addition to previously associated markers located at ERCC1, ERCC2, and SLC22A2, were found to improve predictions in a clinical feature–trained random forest model. Using only clinical data for training the model, an area under the receiver operating characteristic curve of 0.635 (95% confidence interval [CI] = 0.629 to 0.640) was obtained. Retraining the classifier by adding genomics markers increased performance to 0.731 (95% CI = 0.726 to 0.736) and 0.692 (95% CI = 0.688 to 0.696) on the holdout set. Conclusions A clinical and genomics-based machine learning algorithm improved the ability to identify patients at risk of nephrotoxicity compared with using clinical variables alone. Novel genetics associations with cisplatin-induced nephrotoxicity were found for NAT1, NAT2, CNTN6, and CNTN4 that require replication in larger studies before application to clinical practice.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring

2016 ◽  
Vol 25 (6) ◽  
pp. 2750-2766 ◽  
Author(s):  
Hélène Jacqmin-Gadda ◽  
Paul Blanche ◽  
Emilie Chary ◽  
Célia Touraine ◽  
Jean-François Dartigues
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Censored Data ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Disease Onset ◽  
Interval Censoring ◽  
Inverse Probability ◽  
Operating Characteristic Curve ◽  
Semicompeting Risks

Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account for these two issues. In recent years, methods for estimating the time-dependent receiver operating characteristic curve and the associated area under the ROC curve have been extended to account for right censored data and competing risks. In this paper, we show how an approximation allows to use the inverse probability of censoring weighting estimator for semicompeting events with interval censored data. Then, using an illness-death model, we propose two model-based estimators allowing to rigorously handle these issues. The first estimator is fully model based whereas the second one only uses the model to impute missing observations due to censoring. A simulation study shows that the bias for inverse probability of censoring weighting remains modest and may be less than the one of the two parametric estimators when the model is misspecified. We finally recommend the nonparametric inverse probability of censoring weighting estimator as main analysis and the imputation estimator based on the illness-death model as sensitivity analysis.

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