Predicting long-term postsurgical pain by examining the evolution of acute pain

BACKGROUND

Increased acute postoperative pain intensity has been associated with the development of persistent postsurgical pain (PPP) in mechanistic and clinical investigations, but it remains unclear which aspects of acute pain explain this linkage.

METHODS

We analysed clinical postoperative pain intensity assessments using symbolic aggregate approximations (SAX), a graphical way of representing changes between pain states from one patient evaluation to the next, to visualize and understand how pain intensity changes across sequential assessments are associated with the intensity of postoperative pain at 1 (M1) and 6 (M6) months after surgery. SAX-based acute pain transition patterns were compared using cosine similarity, which indicates the degree to which patterns mirror each other.

RESULTS

This single-centre prospective cohort study included 364 subjects. Patterns of acute postoperative pain sequential transitions differed between the 'None' and 'Severe' outcomes at M1 (cosine similarity 0.44) and M6 (cosine similarity 0.49). Stratifications of M6 outcomes by preoperative pain intensity, sex, age group, surgery type and catastrophising showed significant heterogeneity of pain transition patterns within and across strata. Severe-to-severe acute pain transitions were common, but not exclusive, in patients with moderate or severe pain intensity at M6.

CONCLUSIONS

Clinically, these results suggest that individual pain-state transitions, even within patient or procedural strata associated with PPP, may not alone offer good predictive information regarding PPP. Longitudinal observation in the immediate postoperative period and consideration of patient- and surgery-specific factors may help indicate which patients are at increased risk of PPP.

SIGNIFICANCE

Symbolic aggregate approximations of clinically obtained, acute postoperative pain intraday time series identify different motifs in patients suffering moderate to severe pain 6 months after surgery.

The Comparative Analysis and Aggregation of Single-Case Data

The use of single-case methodology has been widely promoted in many disciplines in recent years. Although the use of such a methodology by individual practitioners is feasible and desirable, little attention has been paid to the aggregation of such data for evaluating agencies. This article reports a study using single-case clinical data from an agency, and presents several methods for examining such information. The authors explore both the benefits of such analyses and the pitfalls associated with the proposed methods.

Comparison of automated procedures for ARMA model identification

This article evaluates the performance of three automated proceduresfor ARMA modelidentification commonly available in current versions of SAS for Windows: MINIC, SCAN, and ESACF. Monte Carlo experiments with different model structures, parameter values, and sample sizes were used to compare the methods. On average, the procedures either correctly identified the simulated structures or selected parsimonious nearly equivalent mathematical representations in at least 60% of the trials conducted. For autoregressive models, MINIC achieved the best results. SCAN was superior to the other two procedures for mixed structures. For moving-average processes, ESACF obtained the most correct selections. For all three methods, model identification was less accurate for low dependency than for medium or high dependency processes. The effect of sample size was more pronounced for MINIC than for SCAN and ESACE SCAN and ESACF tended to select higher-order mixed structures in larger samples. These findings are confined to stationary nonseasonal time series.

Model Identification of Integrated ARMA Processes

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can be applied to either nontransformed or differenced series, so the advantages and drawbacks of both procedures were compared. The best results were 79% of correct identifications for SCAN and 80% for ESACF. For some models and parameterizations, the accuracy of SCAN and ESACF was disappointing. The key finding of the study is that both human experts and automated methods provide inconsistent model identifications. Hence an elaborated strategy for model selection combining different techniques was developed and demonstrated on 2 empirical examples.

Computer Programs for Interrupted Time Series Analysis: II A Quantitative Evaluation

Interrupted time series analysis involves repeated observations on a single unit both preceding and following an intervention. Recently developed statistical procedures permit inferential statements about the degree and pattern of the intervention effect. Five different software packages appropriate for interrupted time series analysis were evaluated: TSX, GENTS, BMDP, SAS, and ITSE. Simulated data was generated from 44 types of series representing eleven different ARIMA models, two types of intervention, and two different series' lengths. The five programs are compared with respect to the accuracy of estimation of the error variance, pre-intervention level, post-intervention level, and slope estimates (where appropriate). Three programs produced generally satisfactory results (TSX, GENTS, and SAS), one was generally inaccurate across a wide range of models (ITSE), and one produced some inaccuracies and occasionally failed to complete the analysis (BMDP).