Shock index and its variants as predictors of mortality in severe traumatic brain injury

BACKGROUND

The increase in severe traumatic brain injury (sTBI) incidence is a worldwide phenomenon, resulting in a heavy disease burden in the public health systems, specifically in emerging countries. The shock index (SI) is a physiological parameter that indicates cardiovascular status and has been used as a tool to assess the presence and severity of shock, which is increased in sTBI. Considering the high mortality of sTBI, scrutinizing the predictive potential of SI and its variants is vital.

AIM

To describe the predictive potential of SI and its variants in sTBI.

METHODS

This study included 71 patients (61 men and 10 women) divided into two groups: Survival (S; n = 49) and Non-survival (NS; n = 22). The responses of blood pressure and heart rate (HR) were collected at admission and 48 h after admission. The SI, reverse SI (rSI), rSI multiplied by the Glasgow Coma Score (rSIG), and Age multiplied SI (AgeSI) were calculated. Group comparisons included Shapiro-Wilk tests, and independent samples t-tests. For predictive analysis, logistic regression, receiver operator curves (ROC) curves, and area under the curve (AUC) measurements were performed.

RESULTS

No significant differences between groups were identified for SI, rSI, or rSIG. The AgeSI was significantly higher in NS patients at 48 h following admission (S: 26.32 ± 14.2, and NS: 37.27 ± 17.8; P = 0.016). Both the logistic regression and the AUC following ROC curve analysis showed that only AgeSI at 48 h was capable of predicting sTBI outcomes.

CONCLUSION

Although an altered balance between HR and blood pressure can provide insights into the adequacy of oxygen delivery to tissues and the overall cardiac function, only the AgeSI was a viable outcome-predictive tool in sTBI, warranting future research in different cohorts.

Constructing a prognostic tool for predicting the risk of non-adherence to antiplatelet therapy in discharged patients with coronary heart disease: a retrospective cohort study

Objective

To investigate the incidence and influencing factors affecting the non-adherence behavior of patients with coronary heart disease (CHD) to antiplatelet therapy after discharge and to construct a personalized predictive tool.

Methods

In this retrospective cohort study, 289 patients with CHD who were admitted to the Department of Cardiology of The First Affiliated Hospital of the University of Science and Technology of China between June 2021 and September 2021 were enrolled. The clinical data of all patients were retrospectively collected from the hospital information system, and patients were followed up for 1 year after discharge to evaluate their adherence level to antiplatelet therapy, analyze their present situation and influencing factors for post-discharge adherence to antiplatelet therapy, and construct a nomogram model to predict the risk of non-adherence.

Results

Based on the adherence level to antiplatelet therapy within 1 year after discharge, the patients were divided into the adherence (n = 216) and non-adherence (n = 73) groups. Univariate analysis revealed statistically significant differences between the two groups in terms of variable distribution, including age, education level, medical payment method, number of combined risk factors, percutaneous coronary intervention, duration of antiplatelet medication, types of drugs taken at discharge, and CHD type (P < 0.05). Furthermore, multivariate logistic regression analysis revealed that, except for the medical payment method, all the seven abovementioned variables were independent risk factors for non-adherence to antiplatelet therapy (P < 0.05). The areas under the receiver operating characteristic curve before and after the internal validation of the predictive tool based on the seven independent risk factors and the nomogram were 0.899 (95% confidence interval [CI]: 0.858-0.941) and 0.89 (95% CI: 0.847-0.933), respectively; this indicates that the tool has good discrimination ability. The calibration curve and Hosmer-Lemeshow goodness of fit test revealed that the tool exhibited good calibration and prediction consistency (χ2 = 5.17, P = 0.739).

Conclusion

In this retrospective cohort study, we investigated the incidence and influencing factors affecting the non-adherence behavior of patients with CHD after discharge to antiplatelet therapy. For this, we constructed a personalized predictive tool based on seven independent risk factors affecting non-adherence behavior. The predictive tool exhibited good discrimination ability, calibration, and clinical applicability. Overall, our constructed tool is useful for predicting the risk of non-adherence behavior to antiplatelet therapy in discharged patients with CHD and can be used in personalized intervention strategies to improve patient outcomes.

Performance in Kahoot! activities as predictive of exam performance

BACKGROUND

Game-based learning (GBL) is effective for increasing participation, creativity, and student motivation. However, the discriminative value of GBL for knowledge acquisition has not yet been proven. The aim of this study is to assess the value of Kahoot! as a discriminative tool for formative assessment in medical education in two different subjects.

METHODS

A prospective experimental study was conducted on a sample of 173 students enrolled in neuroanatomy (2021-2022). One hundred twenty-five students individually completed the Kahoot! prior to the final exam. In addition, students enrolled in human histology during two academic courses were included in the study. The control group course (2018-2019) received a traditional teaching methodology (N = 211), while Kahoot! was implemented during 2020-2021 (N = 200). All students completed similar final exams for neuroanatomy and human histology based on theory tests and image exams.

RESULTS

The correlation between the Kahoot score and the final grade was analyzed for all students enrolled in neuroanatomy who completed both exercises. The correlation between the Kahoot exercise and the theory test, image exam and final grade was significantly positive in all cases (r = 0.334 p < 0.001, r = 0.278 p = 0.002 and r = 0.355 p < 0.001, respectively). Moreover, students who completed the Kahoot! exercise obtained significantly higher grades in all parts of the exam. Regarding human histology, the theory tests, image exams and final grades were significantly higher when using Kahoot! versus the "traditional" methodology (p < 0.001, p < 0.001 and p = 0.014, respectively).

CONCLUSIONS

Our study demonstrates for the first time that Kahoot! can be used to improve and predict the final grade in medical education subjects.

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

Objective

This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19).

Material and methods

The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021.

Results

The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%.

Discussion

In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients.

Conclusions

Our easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.

Probability of successful inhaled corticosteroids cessation in preschool wheezers: a predictive score

Nearly all asthma predictive tools estimate the future risk of asthma development. However, there is no tool to predict the probability of successful ICS cessation at an early age. Therefore, we aimed to determine the predictors of successful ICS cessation in preschool wheezers, and developed a simple predictive tool for clinical practice. This was a retrospective cohort study involving preschool wheezers who had undergone an ICS therapeutic trial during 2015-2020 at the University Hospital, Southern, Thailand. A predictive scoring system was developed using a nomogram to estimate the probability of successful ICS cessation. We calculated area under ROC curve and used a calibration plot for assessing the tool's performance. A total of 131 medical records were eligible for analysis. Most of the participants were male (68.9%). More than half of the preschool wheezers had successful ICS cessation after an initial therapeutic trial regimen. The predictors of less successful ICS cessation were perinatal oxygen use [OR 0.10 (0.01, 0.70), P = 0.02], allergic rhinitis [OR 0.20 (0.08, 0.56), P = 0.002], blood eosinophil count > 500 cell/mm³ [OR 0.20 (0.06, 0.67), P = 0.008], and previous ICS use > 6 months [OR 0.30 (0.09, 0.72), P = 0.009].

CONCLUSIONS

Predictors of less successful ICS cessation were the following: perinatal oxygen use, allergic rhinitis, blood eosinophil count > 500 cell/mm³, and previous ICS use > 6 months. A simple predictive score developed in this study may help general practitioners to be more confident in making a decision regarding the discontinuation of ICS after initial therapeutic trials.

WHAT IS KNOWN

• Early allergic sensitization is associated with reduced chances of inhaled corticosteroid cessation at school age. • Prolonged ICS is associated with the emergence of adverse effect and discontinuing too early can result in recurrence symptoms.

WHAT IS NEW

• Requirement of oxygen support within 7 days after birth in term neonate is a postnatal factor associated with less successful ICS cessation. • We propose a simple predictive tool with easily available clinical parameters (perinatal oxygen use, allergic rhinitis, blood eosinophil count, parental asthma history, and duration of previous ICS use) to determine the timing of inhalational corticosteroid cessation in preschool wheezers.

Lack of predictive tools for conventional and targeted cancer therapy: Barriers to biomarker development and clinical translation

Predictive tools, utilising biomarkers, aim to objectively assessthe potentialresponse toa particular clinical intervention in order to direct treatment.Conventional cancer therapy remains poorly served by predictive biomarkers, despite being the mainstay of treatment for most patients. In contrast, targeted therapy benefits from a clearly defined protein target for potential biomarker assessment. We discuss potential data sources of predictive biomarkers for conventional and targeted therapy, including patient clinical data andmulti-omicbiomarkers (genomic, transcriptomic and protein expression).Key examples, either clinically adopted or demonstrating promise for clinical translation, are highlighted. Following this, we provide an outline of potential barriers to predictive biomarker development; broadly discussing themes of approaches to translational research and study/trial design, and the impact of cellular and molecular tumor heterogeneity. Future avenues of research are also highlighted.

The Emergency Surgery Frailty Index (EmSFI): development and internal validation of a novel simple bedside risk score for elderly patients undergoing emergency surgery

BACKGROUND

Frailty assessment has acquired an increasing importance in recent years and it has been demonstrated that this vulnerable profile predisposes elderly patients to a worse outcome after surgery. Therefore, it becomes paramount to perform an accurate stratification of surgical risk in elderly undergoing emergency surgery.

STUDY DESIGN

1024 patients older than 65 years who required urgent surgical procedures were prospectively recruited from 38 Italian centers participating to the multicentric FRAILESEL (Frailty and Emergency Surgery in the Elderly) study, between December 2016 and May 2017. A univariate analysis was carried out, with the purpose of developing a frailty index in emergency surgery called "EmSFI". Receiver operating characteristic curve analysis was then performed to test the accuracy of our predictive score.

RESULTS

784 elderly patients were consecutively enrolled, constituting the development set and results were validated considering further 240 consecutive patients undergoing colorectal surgical procedures. A logistic regression analysis was performed identifying different EmSFI risk classes. The model exhibited good accuracy as regard to mortality for both the development set (AUC = 0.731 [95% CI 0.654-0.772]; HL test χ² = 6.780; p = 0.238) and the validation set (AUC = 0.762 [95% CI 0.682-0.842]; HL test χ² = 7.238; p = 0.299). As concern morbidity, our model showed a moderate accuracy in the development group, whereas a poor discrimination ability was observed in the validation cohort.

CONCLUSIONS

The validated EmSFI represents a reliable and time-sparing tool, despite its discriminative value decreased regarding complications. Thus, further studies are needed to investigate specifically surgical settings, validating the EmSFI prognostic role in assessing the procedure-related morbidity risk.

Simplifying Johanson's roller compaction model to build a "Virtual Roller Compactor" as a predictive tool - Theory and practical application

The purpose of the study is to build a "virtual roller compactor" as a predictive tool to assess the roll force (RF)-maximum pressure (P_max) and RF-ribbon density relationship for pharmaceutical roller compaction. We provided a theoretical basis to demonstrate that, there exists a critical nip angle for a pharmaceutical powder, beyond which the RF-P_max relationship is insensitive to wall friction angle or effective angle of internal friction. We showed that for most pharmaceutical roller compaction, the critical nip angle is lower than 17 degree, and can be exceeded via wall friction elevation, using rolls with non-smooth surface. Under this condition, the original Johanson model can be substantially simplified to a single equation requiring only one material property (compressibility). By performing manufacturing-scale roller compaction using materials with diverse compressibility, we showed that the simplified, friction angle-free model performed similar to the original Johanson model. It can predict the RF-P_max and RF-ribbon density relationship well after applying a correction factor. The predictive tool, in the form of a user-friendly graphical user interface, was created based on the simplified model. The tool was adopted for in-house, bench-scale formulation development and scale-up because of its ease-of-use, good predicting capability, and very low material demand.

Nomogram for Predicting the Overall Survival of Patients With Breast Cancer With Pathologic Nodal Status N3

BACKGROUND

Patients with breast cancer with pathologic N3 (pN3) lymph node status have been proven to have a poor prognosis. This study aimed to establish a nomogram to predict overall survival (OS) in patients with pN3 breast cancer.

MATERIALS AND METHODS

The eligible patients from the Surveillance, Epidemiology, and End Results (SEER) database were randomly divided into training and validation cohorts. χ² tests and survival curves were performed to define the consistency between these 2 cohorts. Univariate and multivariate logistic regressions were carried out to identify the independent clinicopathologic factors of patients with pN3 breast cancer. A nomogram was developed and validated internally and externally by a calibration curve and compared with the seventh edition American Joint Committee on Cancer TNM staging classification in discrimination ability.

RESULTS

Race, age at diagnosis, marital status, grade, T stage, N stage, breast cancer subtype, surgery, radiotherapy, and chemotherapy were independent predictive factors of OS in pN3 breast cancer. We developed a nomogram to predict 1-, 3-, and 5-year OS and further validated it in both cohorts, demonstrating better prediction capacity in OS than that of the seventh edition American Joint Committee on Cancer TNM staging classification (area under the curve in the receiver operating characteristic curve, 0.745 and 0.611 in the training cohort and 0.768 and 0.624 in the validation cohort, respectively).

CONCLUSION

We have developed and validated the first nomogram for predicting the survival of pN3 breast cancer. This nomogram accurately and reliably predicted the OS of patients with pN3 breast cancer. However, more prognostic factors need to be further explored to improve the nomogram.

Development and validation of a screening tool for early identification of bloodstream infection in older patients - a retrospective case-control study

BACKGROUND

Delayed diagnosis of bloodstream infection (BSI) occurs in > 20% of older patients, with misdiagnosis in 35%. Our objective was to develop and validate a clinically useful screening tool to identify older patients with a high probability of having a BSI.

METHODS

Hospitalized patients > 80 years old with BSI (n = 105/group) were evaluated for the tool development in this retrospective matched case-controlled study (learn cohort). The tool was validated in different retrospectively matched case and control patients > 80 years old (n = 120/group) and 65 to 79 years old (n = 250/group) (test cohort). Binary logistic regression was used to develop a screening tool using laboratory and clinical parameters that were significantly associated with BSI (P < 0.05; adjusted odds ratio (OR) > 1); and Classification and Regression Tree (CART) analysis was used to identify parameter breakpoints. Performance metrics were used to evaluate and validate the tool.

RESULTS

The significant parameters associated with BSI were maximum temperature (Tmax)(> 37.55C)(OR = 42.575), neutrophils (> 7.95)(OR = 1.923), a change in level of consciousness (LOC) (Yes = 1, No = 0)(OR = 1.571), blood urea nitrogen (BUN)(> 10.05)(OR = 1.359), glucose (> 7.35)(OR = 1.167), albumin (< 33.5)(OR = 1.038) and alanine aminotransferase (ALT) (> 19.5)(OR = 1.005). The optimal screening tool [Ln (odds of BSI) = - 150.299 + 3.751(Tmax) + 0.654(neutrophils) + 0.452(change in LOC) + 0.307(BUN) + 0.154(glucose) + 0.038(albumin) + 0.005(ALT)] had favorable performance metrics in the learn and test cohorts (sensitivity, specificity and accuracy of 95% in the learn cohort and 77, 89, and 81% in the total test cohort); and performed better than using only temperature and neutrophil count.

CONCLUSIONS

The validated tool had high predictive value which may improve early identification and management of BSI in older patients.

Can We Improve the Preoperative Prediction of Prostate Cancer Recurrence With Multiparametric MRI?

INTRODUCTION

The use of multiparametric magnetic resonance imaging (mpMRI) to assess prostate cancer (PCa) has increased over the past decade. We aimed to assess if preoperative mpMRI lesion score, a variable routinely available for men undergoing pre-biopsy MRI, improves the performance of commonly used preoperative predictive models for PCa recurrence.

PATIENTS AND METHODS

We analyzed data from 372 patients with PCa treated with radical prostatectomy in 2012 to 2017 and assessed with pre-biopsy mpMRI within 6 months prior to surgery. Suspicious areas for cancer were scored on a standardized 5-point scale. Cox regression was used to assess the association between mpMRI score and the risk of postoperative biochemical recurrence. Two different models were tested accounting for factors included in the Kattan nomogram and in the D'Amico risk-classification.

RESULTS

Overall, 53% and 30% of patients were found with a lesion scored 4 or 5 at pre-biopsy mpMRI, respectively. Risk varied widely by mpMRI (29% 2-year risk of biochemical recurrence for a score of 5 vs. 5% for a score of 1-2), and mpMRI score was associated with large hazard ratios after adjusting for stage, grade, and prostate-specific antigen: 1.66, 1.96, and 2.71 for scores 3, 4, and 5, respectively. However, 95% confidence intervals were very wide (0.19-14.20, 0.26-14.65, and 0.36-20.55, respectively) and included 1.

CONCLUSIONS

Our data did not show that preoperative models, commonly used to assess PCa risk, were improved after including the pre-biopsy mpMRI score. However, the value of pre-biopsy mpMRI to improve preoperative risk models should be investigated in larger data sets.

Using landscape scenarios to improve local nitrogen management and planning

Scenario-building is a widely used tool to initiate discussions on future land uses. In scenarios possible futures can be explored and peoples' ideas as well as societal trends can be visualized by the use of maps, pictures and figures. With focus on agricultural nitrogen management, and point of departure in the farmers' decisions-regarding fertilizer inputs, crop rotations, land use, and drainage, landscape scenarios are formulated based on local ideas for future nitrogen management and general prospects for local development. The key research question addressed in this paper is how landscape scenarios can guide farmers to improve nitrogen management in smaller catchments dominated by farming. Participatory modelling was used to develop landscape scenarios, depicting the change of nitrogen emission as a result of changes in landscape management and agricultural practices. In the development of the scenarios we used an ArcMap based tool combining statistical data, experimental knowledge, nitrate leaching modelling and input from local stakeholders on biophysical as well as land use and farm management issues. The scenarios presented are the result of a collaborative planning experiment within the frames of the dNmark research alliance on nitrogen. Three different types of scenarios are presented and discussed and their effects in terms of N reduction are estimated. The three scenarios were called: River valley set-aside, constructed wetlands, and land zonation. All the modelled scenarios are estimated to have a positive effect i.e. a reduction of the level of N leached to the root zone. Based on the experience gathered in the project, the feasibility of using scenarios for future environmental planning in the agricultural landscapes is discussed. Further, this is related to the current discussion in Denmark on geographically targeted nitrogen regulation. It is concluded that the co-creative approach to formulation of scenarios can be an effective way of increasing the knowledge and ownership of possible future solutions, however the cost associated with this planning approach is likely to substantially higher that more traditional planning approaches. Consequently, the estimated transactions costs should be weighed against the expected benefits in terms of more successful implementation.

Tool for predicting Caligus rogercresseyi abundance on salt water salmon farms in Chile

Caligus rogercresseyi is a host-dependent parasite that affects rainbow trout and Atlantic salmon in Chile. Numbers of sea lice on fish increase over time at relatively predictable rates when the environment is conducive to the parasite's survival and fish are not undergoing treatment. We developed a tool for the salmon industry in Chile that predicts the abundance of adult sea lice over time on farms that are relatively isolated. We used data on sea louse abundance collected through the SalmonChile INTESAL sea lice monitoring program to create series of weekly lice counts between lice treatment events on isolated farms. We defined isolated farms as those with no known neighbors within a 10 km seaway distance and no more than two neighbors within a 20 km seaway distance. We defined the time between sea lice treatments as starting the week immediately post treatment and ending the week before a subsequent treatment. Our final dataset of isolated farms consisted of 65 series from 32 farms, between 2009 and 2015. Given an observed abundance at time t = 0, we built a model that predicted 8 consecutive weekly sea louse abundance levels, based on the preceding week's lice prediction. We calibrated the parameters in our model on a randomly selected subset of training data, choosing the parameter combinations that minimized the absolute difference between the predicted and observed sea louse abundance values. We validated the parameters on the remaining, unseen, subset of data. We encoded our model and made it available as a Web-accessible applet for producers. We determined a threshold, based on the upper 97.5% predictive interval, as a guideline for producers using the tool. We hypothesize that if farms exceed this threshold, especially if the sea lice levels are above this threshold 2 and 4 weeks into the model predictions, the sea louse population on the farm is likely influenced by sources other than lice within the farm.

Quantum chemical calculations to determine partitioning coefficients for HgCl2 on iron-oxide aerosols

Gas-to-particle phase partitioning controls the pathways for oxidized mercury deposition from the atmosphere to the Earth's surface. The propensity of oxidized mercury species to transition between these two phases is described by the partitioning coefficient (K_p). Experimental measurements of K_p values for HgCl₂ in the presence of atmospheric aerosols are difficult and time-consuming. Quantum chemical calculations, therefore, offer a promising opportunity to efficiently estimate partitioning coefficients for HgCl₂ on relevant aerosols. In this study, density functional theory (DFT) calculations are used to predict K_p values for HgCl₂ on relevant iron-oxide surfaces. The model is first verified using a NaCl(100) surface, showing good agreement between the calculated (2.8) and experimental (29-43) dimensionless partitioning coefficients at room temperature. Then, the methodology is applied to six atmospherically relevant terminations of α-Fe₂O₃(0001): OH-Fe-R, (OH)₃-Fe-R, (OH)₃-R, O-Fe-R, Fe-O₃-R, and O₃-R (where R denotes bulk ordering). The OH-Fe-R termination is predicted to be the most stable under typical atmospheric conditions, and on this surface termination, a dimensionless HgCl₂K_p value of 5.2 × 10³ at 295 K indicates a strong preference for the particle phase. This work demonstrates DFT as a promising approach to obtain partitioning coefficients, which can lead to improved models for the transport of mercury, as well as for other atmospheric pollutant species, through and between the anthroposphere and troposphere.

Development and Internal Validation of a Nomogram for Predicting Renal Function after Partial Nephrectomy

Loss of renal function can be a clinically impactful event after partial nephrectomy (PN). We aimed to create a model to predict loss of renal function in patients undergoing PN. Data for 1897 consecutive patients who underwent PN with warm ischemia between 2008 and 2017 were extracted from our institutional database. Loss of renal function was defined as upstaging of chronic kidney disease in terms of the estimated glomerular filtration rate (eGFR) at 3 mo after PN. A nomogram was built based on a multivariable model comprising age, sex, body mass index, baseline eGFR, RENAL score, and ischemia time. Interval validation and calibration were performed using data from 676 patients for whom complete data were available. Receiver operator characteristic (ROC) curves with 1000 bootstrap replications were plotted, as well as the observed incidence versus the nomogram-predicted probability. We also applied the extreme training versus test procedure known as leave-one-out cross-validation. After internal validation, the area under the ROC curve was 76%. The model demonstrated excellent calibration. At an upstaging cutoff of 27% probability, upstaging was predicted with a positive predictive value of 86%. PATIENT SUMMARY: In this report, we created a model to predict postoperative loss of renal function after partial nephrectomy for renal tumors. Inputting baseline characteristics and ischemia time into our model allows early identification of patients at higher risk of renal function decline after partial nephrectomy with good predictive power.

A new predictive tool for postoperative radiotherapy in prostate cancer

The standard treatments of localized prostate cancer include surgical resection and/or radiotherapy. Recently in 2016, Zhao et al. described a tool to predict which patients will most likely gain from postoperative radiotherapy. Such a method can personalize treatment plan by maximizing benefit but minimizing harm.

A predictive tool particularly designed for elderly myeloma patients presenting with spinal cord compression

Background

This study was performed to design a predictive tool that allows the estimation of overall survival (OS) of elderly myeloma patients (aged ≥65 years) presenting with myeloma-induced spinal cord compression (SCC).

Methods

One-hundred-and-sixteen patients irradiated for motor deficits of the legs due to myeloma-induced spinal cord compression were retrospectively evaluated. Ten characteristics were analyzed for OS including age, interval between myeloma diagnosis and radiotherapy, other osseous myeloma lesions, myeloma type, gender, time developing motor deficits, number of affected vertebrae, ECOG-PS, pre-radiotherapy ambulatory status, and fractionation regimen. Characteristics that achieved significance on multivariate analysis were included in the predictive tool. The score for each characteristic was obtained from the 1-year OS rate divided by 10. The sum of these scores represented the prognostic score for each patient.

Results

On multivariate analysis, myeloma type (hazard ratio 3.31; 95 %-confidence interval 1.75–6.49; p < 0.001), ECOG-PS (HR 5.33; 95 %-CI 2.67–11.11; p < 0.001), ambulatory status (HR 2.71; 95 % CI 1.65–4.57; p < 0.001), and age (HR 1.95; 95 % CI 1.03–3.78; p = 0.040) were significantly associated with survival. Sum scores ranged from 18 to 32 points. Based on the sum scores, three prognostic groups were designed: 18–19, 21–28 and 29–32 points. The corresponding 1-year survival rates were 0, 43 and 96 %, respectively (p < 0.001).

Conclusions

This new predictive tool has been specifically designed for elderly myeloma patients with SCC. It allows estimating the survival prognosis of this patient group and supports the treating physicians when looking for the optimal treatment approach for an individual patient.

Predicting the survival probability of gastric cancer patients developing metastatic epidural spinal cord compression (MESCC)

To create a tool for estimating the survival of gastric cancer patients developing MESCC, clinical factors were evaluated in 29 patients. Factors were age, gender, performance status, affected vertebrae, ambulatory status, further bone metastases, visceral metastases, time from gastric cancer diagnosis until MESCC and rapidity of developing weakness of legs. On multivariate analyses, visceral metastases (risk ratio: 6.80; p = 0.003) and rapidity of weakening of legs (risk ratio: 2.73; p = 0.023) had a significant effect on survival and were included in the tool. Scoring points for each of the two factors were either 0 or 1, depending on the 6-month survival rates. According to the sum of the points, three groups were built: 0 points (n = 12), 1 point (n = 10) and 2 points (n = 7). Six-month survival rates were 0, 20 and 100 % (p < 0.001). This tool for patients with MESCC from gastric cancer estimates survival probabilities, which is important for tailoring treatment to patients' needs.