Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.
Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided.
(1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS?
Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses.
Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS.
Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments.
Level III, diagnostic study.
Lee CC
，Chen CW
，Yen HK
，Lin YP
，Lai CY
，Wang JL
，Groot OQ
，Janssen SJ
，Schwab JH
，Hsu FM
，Lin WH
... -
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Workplace pedometer interventions for increasing physical activity.
The World Health Organization (WHO) recommends undertaking 150 minutes of moderate-intensity physical activity per week, but most people do not. Workplaces present opportunities to influence behaviour and encourage physical activity, as well as other aspects of a healthy lifestyle. A pedometer is an inexpensive device that encourages physical activity by providing feedback on daily steps, although pedometers are now being largely replaced by more sophisticated devices such as accelerometers and Smartphone apps. For this reason, this is the final update of this review.
To assess the effectiveness of pedometer interventions in the workplace for increasing physical activity and improving long-term health outcomes.
We searched the Cochrane Central Register of Controlled Trials, MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Occupational Safety and Health (OSH) UPDATE, Web of Science, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform from the earliest record to December 2016. We also consulted the reference lists of included studies and contacted study authors to identify additional records. We updated this search in May 2019, but these results have not yet been incorporated. One more study, previously identified as an ongoing study, was placed in 'Studies awaiting classification'.
We included randomised controlled trials (RCTs) of workplace interventions with a pedometer component for employed adults, compared to no or minimal interventions, or to alternative physical activity interventions. We excluded athletes and interventions using accelerometers. The primary outcome was physical activity. Studies were excluded if physical activity was not measured.
We used standard methodological procedures expected by Cochrane. When studies presented more than one physical activity measure, we used a pre-specified list of preferred measures to select one measure and up to three time points for analysis. When possible, follow-up measures were taken after completion of the intervention to identify lasting effects once the intervention had ceased. Given the diversity of measures found, we used ratios of means (RoMs) as standardised effect measures for physical activity.
We included 14 studies, recruiting a total of 4762 participants. These studies were conducted in various high-income countries and in diverse workplaces (from offices to physical workplaces). Participants included both healthy populations and those at risk of chronic disease (e.g. through inactivity or overweight), with a mean age of 41 years. All studies used multi-component health promotion interventions. Eleven studies used minimal intervention controls, and four used alternative physical activity interventions. Intervention duration ranged from one week to two years, and follow-up after completion of the intervention ranged from three to ten months. Most studies and outcomes were rated at overall unclear or high risk of bias, and only one study was rated at low risk of bias. The most frequent concerns were absence of blinding and high rates of attrition. When pedometer interventions are compared to minimal interventions at follow-up points at least one month after completion of the intervention, pedometers may have no effect on physical activity (6 studies; very low-certainty evidence; no meta-analysis due to very high heterogeneity), but the effect is very uncertain. Pedometers may have effects on sedentary behaviour and on quality of life (mental health component), but these effects were very uncertain (1 study; very low-certainty evidence). Pedometer interventions may slightly reduce anthropometry (body mass index (BMI) -0.64, 95% confidence interval (CI) -1.45 to 0.18; 3 studies; low-certainty evidence). Pedometer interventions probably had little to no effect on blood pressure (systolic: -0.08 mmHg, 95% CI -3.26 to 3.11; 2 studies; moderate-certainty evidence) and may have reduced adverse effects (such as injuries; from 24 to 10 per 100 people in populations experiencing relatively frequent events; odds ratio (OR) 0.50, 95% CI 0.30 to 0.84; low-certainty evidence). No studies compared biochemical measures or disease risk scores at follow-up after completion of the intervention versus a minimal intervention. Comparison of pedometer interventions to alternative physical activity interventions at follow-up points at least one month after completion of the intervention revealed that pedometers may have an effect on physical activity, but the effect is very uncertain (1 study; very low-certainty evidence). Sedentary behaviour, anthropometry (BMI or waist circumference), blood pressure (systolic or diastolic), biochemistry (low-density lipoprotein (LDL) cholesterol, total cholesterol, or triglycerides), disease risk scores, quality of life (mental or physical health components), and adverse effects at follow-up after completion of the intervention were not compared to an alternative physical activity intervention. Some positive effects were observed immediately at completion of the intervention periods, but these effects were not consistent, and overall certainty of evidence was insufficient to assess the effectiveness of workplace pedometer interventions.
Exercise interventions can have positive effects on employee physical activity and health, although current evidence is insufficient to suggest that a pedometer-based intervention would be more effective than other options. It is important to note that over the past decade, technological advancement in accelerometers as commercial products, often freely available in Smartphones, has in many ways rendered the use of pedometers outdated. Future studies aiming to test the impact of either pedometers or accelerometers would likely find any control arm highly contaminated. Decision-makers considering allocating resources to large-scale programmes of this kind should be cautious about the expected benefits of incorporating a pedometer and should note that these effects may not be sustained over the longer term. Future studies should be designed to identify the effective components of multi-component interventions, although pedometers may not be given the highest priority (especially considering the increased availability of accelerometers). Approaches to increase the sustainability of intervention effects and behaviours over a longer term should be considered, as should more consistent measures of physical activity and health outcomes.
Freak-Poli R
，Cumpston M
，Albarqouni L
，Clemes SA
，Peeters A
... -
《Cochrane Database of Systematic Reviews》
ResearchGate
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