Identifying At-Risk Subgroups for Acute Postsurgical Pain: A Classification Tree Analysis

Wang, Yan; Liu, Zejun; Chen, Shuanghong; Ye, Xiaoxuan; Xie, Wenyi; Hu, Chunrong; Iezzi, Tony; Jackson, Todd

doi:10.1093/pm/pnx339

Cited by 10 publications

(16 citation statements)

References 81 publications

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“…Preoperative VAS pain can be linked to patients' perception of pain and pain selfefficacy. 1,9,39 Interestingly and conceivably linked, patients in our cohort who reported a history of depression or smoking demonstrated a trend toward increased opioid utilization; however, this did not reach statistical significance (Table 2). 9 This is contrary to previous studies that show depression and nicotine abuse as independent risk factors for increased opioid consumption.…”

Section: Discussionmentioning

confidence: 71%

Prospective Evaluation of Utilization Patterns and Prescribing Guidelines of Opioid Consumption Following Orthopedic Foot and Ankle Surgery

et al. 2018

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Section: Discussionmentioning

confidence: 71%

Prospective Evaluation of Utilization Patterns and Prescribing Guidelines of Opioid Consumption Following Orthopedic Foot and Ankle Surgery

et al. 2018

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“…While traditional predictive models such as logistic and linear regression are feasible, these models can have issues with their implementation and ultimately interpretation by healthcare providers with perceived "black box" results [8,9]. Classification and regression tree analysis (CART) is a technique that is gaining popularity within healthcare due to its ease of interpretation and implementation [8,[10][11][12][13][14][15] Common examples can be noted for guidelines on paediatric head trauma [16][17][18] and paediatric abdominal injuries [19][20][21]. Specifically, CART analysis starts with a large group of individuals, and then makes a series of binary node splits based on some criterion that improves group purity in order to effectively classify individuals.…”

Section: Classification and Regression Tree Analysismentioning

confidence: 99%

“…Importantly, during the CART analysis at each node, the model must determine whether a factor is important through the use of some sort of purity criterion. While there are several methods, the most common for classification trees is the Gini impurity index [8,11,12,16,19,22]. The Gini impurity index determines the optimal means for splitting the members of a node by maximizing the decrease in impurity.…”

Section: Classification and Regression Tree Analysismentioning

confidence: 99%

“…The use of a CART analysis and subsequent production of a classification tree for the care of individuals with lower limb prostheses is unique. There is growing popularity and use of classification trees across healthcare (e.g., see references [8,[10][11][12][13][14][15]). The classification tree developed in this analysis provides probability for determining a patient's functional potential.…”

Section: Model Strengthsmentioning

confidence: 99%

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Mobility Analysis of AmpuTees (MAAT 4): classification tree analysis for probability of lower limb prosthesis user functional potential

Wurdeman

Stevens

Campbell

2019

Disability and Rehabilitation: Assistive Technology

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Purpose: To develop a predictive model to inform the probability of lower limb prosthesis users' functional potential for ambulation. Materials and Methods: A retrospective analysis of a database of outcomes for 2770 lower limb prosthesis users was used to inform a classification and regression tree analysis. Gender, age, height, weight, body mass index adjusted for amputation, amputation level, cause of amputation, comorbid health status and functional mobility score [Prosthetic Limb Users Survey of Mobility (PLUS-M TM)] were entered as potential predictive variables. Patient K-Level was used to assign dependent variable status as unlimited community ambulator (i.e., K3 or K4) or limited community/household ambulator (i.e., K1 or K2). The classification tree was initially trained from 20% of the sample and subsequently tested with the remaining sample. Results: A classification tree was successfully developed, able to accurately classify 87.4% of individuals within the model's training group (standard error 1.4%), and 81.6% within the model's testing group (standard error 0.82%). Age, PLUS-M TM T-score, cause of amputation and body weight were retained within the tree logic. Conclusions: The resultant classification tree has the ability to provide members of the clinical care team with predictive probabilities of a patient's functional potential to help assist care decisions. ä IMPLICATIONS FOR REHABILITATION Classification and regression tree analysis is a simple analytical tool that can be used to provide simple predictive models for patients with a lower limb prosthesis. The resultant classification tree had an 81.6% (standard error 0.82%) accuracy predicting functional potential as an unlimited community ambulator (i.e., K3 or K4) or limited community/ household ambulator (i.e., K1 or K2) in an unknown group of 2770 lower limb prosthesis users. The resultant classification tree can assist with the rehabilitation team's care planning providing probabilities of functional potential for the lower limb prosthesis user.

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“…Items are rated from 1 (strongly disagree) to 4 (strongly agree). PCA on a China-based sample found that nine TSK-11 items loaded on one component (Wang et al, 2018). Hence, total TSK scores were based on these nine items.…”

Section: Methodsmentioning

confidence: 99%

Factor structure, correlates, and incremental validity of the Pain Appraisal Inventory-Short Form

Chen

et al. 2018

J Health Psychol

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We assessed the factor structure, correlates, and incremental validity of the Pain Appraisal Inventory in Chinese adult chronic pain samples. In an initial exploratory factor analysis sample ( N = 301), the original two-component (threat, challenge) 16-item Pain Appraisal Inventory and a 10-item short form (Pain Appraisal Inventory-Short Form) were supported. Within a confirmatory factor analysis sample ( N = 285), uniformly acceptable fits were observed only for the Pain Appraisal Inventory-Short Form. Furthermore, Pain Appraisal Inventory-Short Form threat and challenge subscales had significant correlations with conceptually related measures and added to prediction models for pain-related coping and adjustment, independent of other pain belief scales. Together, results indicated that the Pain Appraisal Inventory-Short Form has utility in Chinese chronic pain samples.

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Identifying At-Risk Subgroups for Acute Postsurgical Pain: A Classification Tree Analysis

Abstract: Together, these findings underscored the potential utility of CTA as a means of identifying patient subgroups with higher and lower risk for severe acute postoperative pain based on interacting characteristics.

Cited by 10 publications

References 81 publications

Prospective Evaluation of Utilization Patterns and Prescribing Guidelines of Opioid Consumption Following Orthopedic Foot and Ankle Surgery

Prospective Evaluation of Utilization Patterns and Prescribing Guidelines of Opioid Consumption Following Orthopedic Foot and Ankle Surgery

Mobility Analysis of AmpuTees (MAAT 4): classification tree analysis for probability of lower limb prosthesis user functional potential

Factor structure, correlates, and incremental validity of the Pain Appraisal Inventory-Short Form

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