Effects of Multidisciplinary Biopsychosocial Rehabilitation on Short-Term Pain and Disability in Chronic Low Back Pain: A Systematic Review with Network Meta-Analysis.

Chronic low back pain (CLBP) is a significant public health issue, with prevalence intensifying due to an ageing global population, amassing approximately 619 million cases in 2020 and projected to escalate to 843 million by 2050. In this study, we analyzed the effects of multidisciplinary biopsychosocial rehabilitation (MBR) on pain and disability. To address this question, we conducted a PRISMA-guided systematic review and random-effect network meta-analysis on studies collected from six electronic databases. The network comprised diverse MBR modalities (behavioral, educational, and work conditioning) alongside exercise therapy (ET), minimal intervention, and usual care, with pain and disability as outcomes. Ninety-three studies were included, encompassing a total of 8059 participants. The NMA substantiated that both ET and MBR modalities were effective in alleviating CLBP, with education-oriented MBR emerging as the most efficacious for pain mitigation (MD = 18.29; 95% CI = 13.70; 22.89) and behavior-focused MBR being the most efficacious for disability reduction (SMD = 0.88; 95% CI = 0.46; 1.30). Nevertheless, the discerned differences amongst the treatments were minimal and uncertain, highlighting that no modality was definitively superior to the others. Given the intricate nature of CLBP, embodying various facets, our findings advocate for a combined therapeutic approach to optimize treatment efficacy.

Random cancers as supported by registry data.

There has been considerable interest in recent years in quantifying the rate of unavoidable or so-called random cancers, as opposed to cancers linked to environmental, genetic or other factors. We propose a data-based approach to estimate an upper limit to this probability, based on an analysis of multiple registry data. The argument is that the cumulative hazards for random cancers cannot exceed the minimum reliable cumulative hazard observed across the registries. We propose a Monte Carlo method to identify this upper limit and apply the method to data on nine different cancers recorded by 423 registries. We compare our values with estimates obtained from a random mutations argument.

Explained variation of excess hazard models.

The availability of longstanding collection of detailed cancer patient information makes multivariable modelling of cancer-specific hazard of death appealing. We propose to report variation in survival explained by each variable that constitutes these models. We adapted the ranks explained (RE) measure to the relative survival data setting, ie, when competing risks of death are accounted for through life tables from the general population. RE is calculated at each event time. We introduce weights for each death reflecting its probability to be a cancer death. RE varies between -1 and +1 and can be reported at given times in the follow-up and as a time-varying measure from diagnosis onward. We present an application for patients diagnosed with colon or lung cancer in England. The RE measure shows reasonable properties and is comparable in both relative and cause-specific settings. One year after diagnosis, RE for the most complex excess hazard models reaches 0.56, 95% CI: 0.54 to 0.58 (0.58 95% CI: 0.56-0.60) and 0.69, 95% CI: 0.68 to 0.70 (0.67, 95% CI: 0.66-0.69) for lung and colon cancer men (women), respectively. Stage at diagnosis accounts for 12.4% (10.8%) of the overall variation in survival among lung cancer patients whereas it carries 61.8% (53.5%) of the survival variation in colon cancer patients. Variables other than performance status for lung cancer (10%) contribute very little to the overall explained variation. The proportion of the variation in survival explained by key prognostic factors is a crucial information toward understanding the mechanisms underpinning cancer survival. The time-varying RE provides insights into patterns of influence for strong predictors.

Survival and event-free survival of patients with peripheral arterial disease undergoing prevention of cardiovascular disease.

BACKGROUND: Patients with peripheral arterial disease (PAD) are at very high risk for cardiovascular events. How do patients with PAD differ from age- and sex-matched controls in survival, major ischemic events and revascularization procedures when both groups were managed according to the European guidelines on cardiovascular disease prevention? METHODS: Patients with PAD (N.=742) and 713 age and sex-matched control subjects without PAD, both groups aged 65±9 years at inclusion, were managed for 5 years according to the European guidelines on cardiovascular disease prevention and evaluated yearly for occurrence of death, non-fatal major ischemic events and revascularization procedures (minor events). RESULTS: In the PAD group, the 5-year survival was 84.7% (CI 82.1-87.3%) vs. 93.3% (CI 91.5-95.2%) in the control group, P<0.001. In the PAD group the proportion of cardiovascular deaths did not differ significantly from non-cardiovascular deaths (6.9 vs. 8.4%, P=0.14), while in the control group cardiovascular deaths were less frequent (2.4 vs. 4.3%, P=0.05). The groups differed in 5-year major event-free survival: 76.7% (CI 73.7-79.8%) in PAD vs. 89.9% (CI 87.7 -92.2%) in controls, P<0.001, and in event-free survival: 56.2% (CI 52.7-59.9%) in PAD vs. 82.4% (CI 79.9-85.3%) in controls, P<0.001. CONCLUSIONS: Patients with PAD had a higher risk of all-cause death, major and minor non-fatal cardiovascular events compared to control subjects. In our group, cardiovascular events were not the leading cause of death in patients with PAD (ClinicalTrials.gov number NCT00761969.).

A note on bias of measures of explained variation for survival data.

Papers evaluating measures of explained variation, or similar indices, almost invariably use independence from censoring as the most important criterion. And they always end up suggesting that some measures meet this criterion, and some do not, most of the time leading to a conclusion that the first is better than the second. As a consequence, users are offered measures that cannot be used with time-dependent covariates and effects, not to mention extensions to repeated events or multi-state models. We explain in this paper that the aforementioned criterion is of no use in studying such measures, because it simply favors those that make an implicit assumption of a model being valid everywhere. Measures not making such an assumption are disqualified, even though they are better in every other respect. We show that if these, allegedly inferior, measures are allowed to make the same assumption, they are easily corrected to satisfy the 'independent-from-censoring' criterion. Even better, it is enough to make such an assumption only for the times greater than the last observed failure time τ, which, in contrast with the 'preferred' measures, makes it possible to use all the modeling flexibility up to τ and assume whatever one wants after τ. As a consequence, we claim that some of the measures being preferred as better in the existing reviews are in fact inferior.

Explained variation for recurrent event data.

Although there are many suggested measures of explained variation for single-event survival data, there has been little attention to explained variation for recurrent event data. We describe an existing rank-based measure and we investigate a new statistic based on observed and expected event count processes. Both methods can be used for all models. Adjustments for missing data are proposed and demonstrated through simulation to be effective. We compare the population values of the two statistics and illustrate their use in comparing an array of non-nested models for data on recurrent episodes of infant diarrhoea.

The Use of Competency Models to Assess Leadership in Nursing.

BACKGROUND: The efficiency of the health care system is significantly dependent on the appropriate leadership and guidance of employees. One of the most frequently used new approaches in human resources management is the study of competencies and competency models. The aim of this research is to develop a competency model for leaders in nursing, and to compare it with the leadership competency model for state administration. METHODS: A survey was conducted among 141 nurse leaders in Slovenia. The respondents were asked to complete questionnaire with 95 leadership behaviours that form the leadership competency model for leaders in nursing. The data were analysed by ANOVA and Tukey's honestly significant differences test. RESULTS: The levels of competencies set for themselves by leaders at the third leadership level in nursing (leaders of small units and teams) are significantly lower than those set by all other leaders, both in nursing and in state administration. Statistically significant differences were apparent in the majority of areas. CONCLUSION: Within the context of the comparison of competency models, the greatest need for training can be observed at the third level of leadership in nursing. A comparison of models formulated in this way enables the exchange of good practices among leaders from various professional groups and easier identification of the training needs of individual groups of leaders in public administration. The proposed concept is designed to significantly simplify and unify the building of competency-based leadership models in public sector.

On estimation in relative survival.

Estimation of relative survival has become the first and the most basic step when reporting cancer survival statistics. Standard estimators are in routine use by all cancer registries. However, it has been recently noted that these estimators do not provide information on cancer mortality that is independent of the national general population mortality. Thus they are not suitable for comparison between countries. Furthermore, the commonly used interpretation of the relative survival curve is vague and misleading. The present article attempts to remedy these basic problems. The population quantities of the traditional estimators are carefully described and their interpretation discussed. We then propose a new estimator of net survival probability that enables the desired comparability between countries. The new estimator requires no modeling and is accompanied with a straightforward variance estimate. The methods are described on real as well as simulated data.

Has equity in relative survival improved over time in Finland - a methodological exercise.

BACKGROUND: Population-based relative survival is widely used as a method of monitoring the success of cancer control. This success may not be relevant only for an entire country but also regional developments over time are of interest. It would not only be important that the relative survival improved but also that the differences between regions decreased over time. METHODS: In this paper the authors show how relative survival methods can be used to study such differences. In addition to standard methods, some more recently introduced approaches are used, most notably a method for checking the goodness of fit of the relative survival model. This gives confidence in the obtained results and provides additional insight when assumptions are not met. RESULTS: An analysis of cancers of the colon and ovary by cancer control region in Finland in 1953-2003 shows an overall improvement in relative survival, accompanied in colon cancer also by a decrease of differences in relative survival between the regions. Thus, the desired course was observed in colon cancer but not in cancer of the ovary. CONCLUSIONS: These results, applied to further sites, should lead to investigation of differences in cancer control policies between regions.

Segmentation priors from local image properties: without using bias field correction, location-based templates, or registration.

We present a novel approach for generating information about a voxel's tissue class membership based on its signature--a collection of local image textures estimated over a range of neighborhood sizes. The approach produces a form of tissue class priors that can be used to initialize and regularize image segmentation. The signature-based approach is a departure from current location-based methods, which derive tissue class likelihoods based on a voxel's location in standard template space. To use location-based priors, one needs to register the volume in question to the template space, and estimate the image intensity bias field. Two optimizations, over more than a thousand parameters, are needed when high order nonlinear registration is employed. In contrast, the signature-based approach is independent of volume orientation, voxel position, and largely insensitive to bias fields. For these reasons, the approach does not require the use of population derived templates. The prior information is generated from variations in image texture statistics as a function of spatial scale, and an SVM approach is used to associate signatures with tissue types. With the signature-based approach, optimization is needed only during the training phase for the parameter estimation stages of the SVM hyperplanes, and associated PDFs; a training process separate from the segmentation step. We found that signature-based priors were superior to location-based ones aligned under favorable conditions, and that signature-based priors result in improved segmentation when replacing location-based ones in FAST (Zhang et al., 2001), a widely used segmentation program. The software implementation of this work is freely available as part of AFNI http://afni.nimh.nih.gov.

A measure of explained variation for event history data.

There is no shortage of proposed measures of prognostic value of survival models in the statistical literature. They come under different names, including explained variation, correlation, explained randomness, and information gain, but their goal is common: to define something analogous to the coefficient of determination R(2) in linear regression. None however have been uniformly accepted, none have been extended to general event history data, including recurrent events, and many cannot incorporate time-varying effects or covariates. We present here a measure specifically tailored for use with general dynamic event history regression models. The measure is applicable and interpretable in discrete or continuous time; with tied data or otherwise; with time-varying, time-fixed, or dynamic covariates; with time-varying or time-constant effects; with single or multiple event times; with parametric or semiparametric models; and under general independent censoring/observation. For single-event survival data with neither censoring nor time dependency it reduces to the concordance index. We give expressions for its population value and the variance of the estimator and explore its use in simulations and applications. A web link to R software is provided.

An approach to estimation in relative survival regression.

The goal of relative survival methodology is to compare the survival experience of a cohort with that of the background population. Most often an additive excess hazard model is employed, which assumes that each person's hazard is a sum of 2 components--the population hazard obtained from life tables and an excess hazard attributable to the specific condition. Usually covariate effects on the excess hazard are assumed to have a proportional hazards structure with parametrically modelled baseline. In this paper, we introduce a new fitting procedure using the expectation-maximization algorithm, treating the cause of death as missing data. The method requires no assumptions about the baseline excess hazard thus reducing the risk of bias through misspecification. It accommodates the possibility of knowledge of cause of death for some patients, and as a side effect, the method yields an estimate of the ratio between the excess and the population hazard for each subject. More importantly, it estimates the baseline excess hazard flexibly with no additional degrees of freedom spent. Finally, it is a generalization of the Cox model, meaning that all the wealth of options in existing software for the Cox model can be used in relative survival. The method is applied to a data set on survival after myocardial infarction, where it shows how a particular form of the hazard function could be missed using the existing methods.

International Atomic Energy Agency-sponsored multination study of intra-arterial rhenium-188-labeled lipiodol in the treatment of inoperable hepatocellular carcinoma: results with special emphasis on prognostic value of dosimetric study.

A multicenter study was sponsored by the International Atomic Energy Agency (IAEA) to assess the safety and efficacy of transarterial rhenium-188 ((188)Re) HDD lipiodol (radioconjugate to lipiodol using an HDD kit) in the treatment of unresectable hepatocellular carcinoma. During 5 years, 185 patients received at least 1 treatment of radioconjugate, and 51 were retreated. The level of radioconjugate administered was based on radiation-absorbed dose to critical normal organs, calculated after a "scout" dose of radioconjugate. The total injected activity, including the scout dose during the first treatment, ranged from 21 to 364 mCi (mean, 108 mCi/4 GBq). Immediate and late side-effects were minimal. Tumor size could be evaluated in 88 patients. Among these patients, the objective response rate was 25%; stable disease was observed in 53% and tumor progression in 22%. With a median follow-up of 455 days, the estimated 12- and 24-month overall survival was 46% and 23%. This multicenter study shows that (188)Re lipiodol is a safe and cost-effective method to treat primary hepatocellular carcinoma via the transarterial route and requires further evaluation by treatment of greater numbers of patients.

Intra-arterial rhenium-188 lipiodol in the treatment of inoperable hepatocellular carcinoma: results of an IAEA-sponsored multination study.

PURPOSE: Intra-arterial injections (IAI) of 131I-lipiodol is effective in treating hepatocellular carcinoma patients, but is expensive and requires a 7-day hospitalization in a radioprotection room. 188Re is inexpensive, requires no patient isolation, and can be used with lipiodol. METHODS AND MATERIALS: This International Atomic Energy Agency-sponsored phase II trial aimed to assess the safety and the efficacy of a radioconjugate 188Re + lipiodol (188Re-Lip) in a large cohort of hepatocellular carcinoma patients from developing countries. A scout dose is used to determine the maximal tolerated dose (lungs <12 Gy, normal liver <30 Gy, bone marrow <1.5 Gy) and then the delivery of the calculated activity. Efficacy was assessed using response evaluation criteria in solid tumor (RECIST) and alpha-feto-protein (alpha FP) levels and severe adverse events were graded using the Common Toxicity Criteria of the National Cancer Institute scale v2.0. RESULTS: The trial included 185 patients from eight countries. The procedure was feasible in all participating centers. One treatment was given to 134 patients; 42, 8, and 1 received two, three, and four injections, respectively. The injected activity during the first treatment was 100 mCi. Tolerance was excellent. We observed three complete responses and 19 partial responses (22% of evaluable patients, 95% confidence interval 16-35%); 1- and 2-year survivals were 46% and 23%. Some factors affected survival: country of origin, existence of a cirrhosis, Cancer of the Liver Italian Program score, tumor dose, absence of progression, and posttreatment decrease in alpha FP level. CONCLUSIONS: IAI of 188Re-Lip in developing countries is feasible, safe, cost-effective, and deserves a phase III trial.

Making relative survival analysis relatively easy.

In survival analysis we are interested in time from the beginning of an observation until certain event (death, relapse, etc.). We assume that the final event is well defined, so that we are never in doubt whether the final event has occurred or not. In practice this is not always true. If we are interested in cause-specific deaths, then it may sometimes be difficult or even impossible to establish the cause of death, or there may be different causes of death, making it impossible to assign death to just one cause. Suicides of terminal cancer patients are a typical example. In such cases, standard survival techniques cannot be used for estimation of mortality due to a certain cause. The cure to the problem are relative survival techniques which compare the survival experience in a study cohort to the one expected should they follow the background population mortality rates. This enables the estimation of the proportion of deaths due to a certain cause. In this paper, we briefly review some of the techniques to model relative survival, and outline a new fitting method for the additive model, which solves the problem of dependency of the parameter estimation on the assumption about the baseline excess hazard. We then direct the reader's attention to our R package relsurv that provides functions for easy and flexible fitting of all the commonly used relative survival regression models. The basic features of the package have been described in detail elsewhere, but here we additionally explain the usage of the new fitting method and the interface for using population mortality data freely available on the Internet. The combination of the package and the data sets provides a powerful informational tool in the hands of a skilled statistician/informatician.

Peripheral arterial disease and ankle-brachial pressure index as predictors of mortality in residents of Metlika County, Slovenia.

AIM: To test how the presence of peripheral arterial disease predicted mortality of middle-aged and elderly residents of Metlika county, a rural area in southeastern Slovenia. METHODS: In 1987, we interviewed and examined a representative cohort of 646 subjects aged 45-80 years at inclusion without overt coronary or cerebrovascular disease, for cardiovascular risk factors and measured the ankle-brachial pressure index (ABPI). Peripheral arterial disease was defined as ABPI<0.90. The subjects were followed up 15 years or until death. All-cause mortality and cardiovascular mortality were assessed and compared between subjects with and without peripheral arterial disease in a multivariate model. RESULTS: There were 580 subjects with normal ABPI and 66 subjects with peripheral arterial disease, among which 49 were asymptomatic and 17 had intermittent claudication. Because subjects with peripheral arterial disease were on average 10 years older than those without peripheral arterial disease, the mere presence of peripheral arterial disease was not an independent predictor of mortality. However, there was a significant interaction of peripheral arterial disease with age, with a more pronounced adverse prognostic effect of peripheral arterial disease in younger than in older age groups. For a 55-year-old subject with peripheral arterial disease, the hazard ratio of dying from any cause in the follow-up period was 2.44 (95% confidence interval [CI], 1.15-4.96) in comparison to an age-matched subject without peripheral arterial disease, but at 75 years of age, the hazard ratio decreased to only 0.71 (95% CI, 0.46-1.09). For cardiovascular mortality, the hazard ratio in the presence of peripheral arterial disease was 6.05 (95% CI, 1.87-16.27) at 55 years and 0.92 (95% CI, 0.54-1.52) at 75 years. Among patients with peripheral arterial disease, each decrement of ABPI at inclusion by 0.10 significantly increased the cardiovascular mortality after 15 years by 30% (P = 0.038). CONCLUSION: Peripheral arterial disease, even asymptomatic, is an important predictor of adverse cardiovascular prognosis in relatively young patients. Reduced ABPI is a strong, independent predictor of cardiovascular mortality in all patients with peripheral arterial disease.

Relative survival analysis in R.

Relative survival techniques are used to compare the survival experience in a study cohort with the one expected should they follow the background population mortality rates. The techniques are especially useful when the cause-specific death information is not accurate or not available since they provide a measure of excess mortality in a group of patients with a certain disease. There are several approaches to modeling relative survival, but there is no widely used statistical package that would incorporate the relevant techniques. The existing software was mostly written by the authors of different methods, in different computer languages and with different requirements for the data input, which makes it almost impossible for a user to choose between available models. We describe our R package relsurv that provides functions for easy and flexible fitting of several relative survival regression models.

The risk of lower mental outcome in infantile spasms increases after three weeks of hypsarrhythmia duration.

To assess the correlation between hypsarrhythmia duration and mental outcome in infantile spasms (IS) the medical records of 48 infants with IS were reviewed retrospectively and psychological assessments undertaken at follow-up at the age of 3 to 13 years. We found 18 (38%) cryptogenic IS cases with typical hypsarrhythmia and 30 symptomatic with modified hypsarrhythmia-further classified into 15 cases as multifocal, 10 as pseudoperiodic and 5 as unilateral hypsarrhythmia. A short treatment lag (one to two weeks) occurred in 25, three to four weeks in 10 cases. Spasms ceased within one month after treatment in 23 infants. At follow-up 15 children had normal mental outcome (borderline included). A correlation between hypsarrhythmia duration longer than three weeks and lower mental outcome was found using the logistic regression model. The duration of hypsarrhythmia represents a sensitive prognostic parameter in IS; the risk of mental retardation increases after three weeks of hypsarrhythmia.