Clinical performance of the iPREDICTLIVING tool for the prediction of the post-transplant recipient and living donor outcomes in a European cohort

A living donor kidney transplant (LDKT) is the best treatment for ESRD. A prediction tool based on clinical and demographic data available pre-KT was developed in a Norwegian cohort with three different models to predict graft loss, recipient death, and donor candidate's risk of death, the iPREDICTLIVING tool. No external validations are yet available. We sought to evaluate its predictive performance in our cohort of 352 pairs LKDT submitted to KT from 1998 to 2019. The model for censored graft failure (CGF) showed the worse discriminative performance with Harrell's C of .665 and a time-dependent AUC of .566, with a calibration slope of .998. For recipient death, at 10 years, the model had a Harrell's C of .776, a time-dependent AUC of .773, and a calibration slope of 1.003. The models for donor death were reasonably discriminative, although with a poor calibration, particularly for 20 years of death, with a Harrell's C of .712 and AUC of .694 with a calibration slope of .955. These models have moderate discriminative and calibration performance in our population. The tool was validated in this Northern Portuguese cohort, Caucasian, with a low incidence of diabetes and other comorbidities. It can improve the informed decision-making process at the living donor consultation joining clinical and other relevant information.

Prediction tools and risk stratification in epilepsy surgery

OBJECTIVE

This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2).

METHODS

We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3-4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%-70%), and low (ESGS = 2, SFS = 0-1, ESNs < 40%). We compared the three tiers as stratified by these tools, concerning the proportion of patients who were operated on, and for the proportion of patients who became free of disabling seizures.

RESULTS

The concordance indexes for the various versions of the nomograms were between .56 and .69. Both scales (ESGS, SFS) and nomograms accurately stratified the patients for becoming free of disabling seizures, with significant differences among the three tiers (p < .05). In addition, ESGS and the modified ESN accurately stratified the patients for having been offered surgery, with significant difference among the three tiers (p < .05).

SIGNIFICANCE

ESGS and the modified ESN (at thresholds of 40% and 70%) stratify patients undergoing presurgical evaluation into three tiers, with high, moderate, and low chance for favorable outcome, with significant differences between the groups concerning having surgery and becoming free of disabling seizures. Stratifying patients for epilepsy surgery has the potential to help select the optimal candidates in underprivileged areas and better allocate resources in developed countries.

Pulmonary embolism: a practical approach to update risk stratification and treatment decisions based on the guidelines

INTRODUCTION

Pulmonary embolism (PE) is a prevalent condition with a substantial morbi-mortality worldwide. Proper risk stratification of PE is essential for identifying the most suitable therapeutic strategy and the optimal care setting for the patient. This process entails evaluating various factors, including symptoms, comorbidities, and right heart dysfunction.

AREAS COVERED

This review assesses the tools and methods utilized to identify and stratify individuals based on the probability of developing deterioration or death related to PE. Current guidelines divide PE into three groups: high-risk (previously termed massive) PE, intermediate-risk (sub-massive) PE, and low-risk PE. Various risk scores, such as the simplified pulmonary embolism severity index (sPESI), Bova score, and the FAST score (incorporating Heart-Fatty Acid binding protein [H-ABP], Syncope, Tachycardia), aid in identifying patients at higher risk. Additionally, the Hestia score is instrumental in pinpointing low-risk patients.

EXPERT OPINION

Presently, there is a dearth of high-quality frameworks for the optimal management and treatment of PE patients at risk of hemodynamic collapse. A consortium of experts is in the process of formulating a new conceptual model for risk stratification, taking into account a comprehensive array of variables and outcomes to facilitate more individualized management of acute PE.

Artificial Intelligence and Predictive Modeling in Spinal Oncology: A Narrative Review

BACKGROUND

Artificial intelligence (AI) tremendously influences our daily lives and the medical field, changing the scope of medicine. One of the fields where AI, and, in particular, predictive modeling, holds great promise is spinal oncology. An accurate patient prognosis is essential to determine the optimal treatment strategy for patients with spinal metastases. Multiple studies demonstrated that the physician's survival predictions are inaccurate, which resulted in the development of numerous predictive models. However, difficulties arise when trying to interpret these models and, more importantly, assess their quality.

OBJECTIVE

To provide an overview of all stages and challenges in developing predictive models using the Skeletal Oncology Research Group machine learning algorithms as an example.

METHODS

A narrative review of all relevant articles known to the authors was conducted.

RESULTS

Building a predictive model consists of 6 stages: preparation, development, internal validation, presentation, external validation, and implementation. During validation, the following measures are essential to assess the model's performance: calibration, discrimination, decision curve analysis, and the Brier score. The structured methodology in developing, validating, and reporting the model is vital when building predictive models. Two principal guidelines are the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis checklist and the prediction model risk of bias assessment. To date, many predictive modeling studies lack the right validation measures or improperly report their methodology.

CONCLUSIONS

A new health care age is being ushered in by the rapid advancement of AI and its applications in spinal oncology. A myriad of predictive models are being developed; however, the subsequent stages, quality of validation, transparent reporting, and implementation still need improvement.

CLINICAL RELEVANCE

Given the rapid rise and use of AI prediction models in patient care, it is valuable to know how to assess their quality and to understand how these models influence clinical practice. This article provides guidance on how to approach this.

LEVEL OF EVIDENCE: 4

Hospital Practices for Respiratory Isolation for Patients With Suspected Tuberculosis and Potential Application of Prediction Models

Hospitalized persons with suspected pulmonary tuberculosis (PTB) are placed in airborne isolation to prevent nosocomial infection, as recommended by the Centers for Disease Control and Prevention (CDC). There is significant evidence that clinicians overuse this resource due to an abundance of caution when confronted with a patient with possible PTB. Many researchers have developed predictive tools based on clinical and radiographic data to assist clinicians in deciding which patients to place in respiratory isolation. We assessed the isolation practices for an urban hospital serving a large immigrant population and then retrospectively applied seven previously derived prediction models of isolation of PTB to our population. Our current clinical practice results in 76% of patients with PTB being placed in isolation on admission. However, 208 patients without PTB were placed in isolation unnecessarily for a total of 584 days. Four models had sensitivities greater than 90%, and two models had sensitivities of 100%. The use of these models would have potentially saved more than 150 days of patient isolation per year.

Prediction of Noninvasive Ventilation Failure in COVID-19 Patients: When Shall We Stop?

INTRODUCTION

In coronavirus disease 2019 (COVID-19), there are no tools available for the difficult task of recognizing which patients do not benefit from maintaining respiratory support, such as noninvasive ventilation (NIV). Identifying treatment failure is crucial to provide the best possible care and optimizing resources. Therefore, this study aimed to build a model that predicts NIV failure in patients who did not progress to invasive mechanical ventilation (IMV).

METHODS

This retrospective observational study included critical COVID-19 patients treated with NIV who did not progress to IMV. Patients were admitted to a Portuguese tertiary hospital between October 1, 2020, and March 31, 2021. The outcome of interest was NIV failure, defined as COVID-19-related in-hospital death. A binary logistic regression was performed, where the outcome (mortality) was the dependent variable. Using the independent variables of the logistic regression a decision-tree classification model was implemented.

RESULTS

The study sample, composed of 103 patients, had a mean age of 66.3 years (SD=14.9), of which 38.8% (40 patients) were female. Most patients (82.5%) were autonomous for basic activities of daily living. The prediction model was statistically significant with an area under the curve of 0.994 and a precision of 0.950. Higher age, a higher number of days with increases in the fraction of inspired oxygen (FiO₂), a higher number of days of maximum expiratory positive airway pressure, a lower number of days on NIV, and a lower number of days from disease onset to hospital admission were, with statistical significance, associated with increased odds of death. A decision-tree classification model was then obtained to achieve the best combination of variables to predict the outcome of interest.

CONCLUSIONS

This study presents a model to predict death in COVID-19 patients treated with NIV in patients who did not progress to IMV, based on easily applicable variables that mainly reflect patients' evolution during hospitalization. Along with the decision-tree classification model, these original findings may help clinicians define the best therapeutical approach to each patient, prioritizing life-comforting measures when adequate, and optimizing resources, which is crucial within limited or overloaded healthcare systems. Further research is needed on this subject of treatment failure, not only to understand if these results are reproducible but also, in a broader sense, helping to fill this gap in modern medicine guidelines.

Circr, a Computational Tool to Identify miRNA:circRNA Associations

Circular RNAs (circRNAs) are known to act as important regulators of the microRNA (miRNA) activity. Yet, computational resources to identify miRNA:circRNA interactions are mostly limited to already annotated circRNAs or affected by high rates of false positive predictions. To overcome these limitations, we developed Circr, a computational tool for the prediction of associations between circRNAs and miRNAs. Circr combines three publicly available algorithms for de novo prediction of miRNA binding sites on target sequences (miRanda, RNAhybrid, and TargetScan) and annotates each identified miRNA:target pairs with experimentally validated miRNA:RNA interactions and binding sites for Argonaute proteins derived from either ChIPseq or CLIPseq data. The combination of multiple tools for the identification of a single miRNA recognition site with experimental data allows to efficiently prioritize candidate miRNA:circRNA interactions for functional studies in different organisms. Circr can use its internal annotation database or custom annotation tables to enhance the identification of novel and not previously annotated miRNA:circRNA sites in virtually any species. Circr is written in Python 3.6 and is released under the GNU GPL3.0 License at https://github.com/bicciatolab/Circr.

A Scoring Tool That Identifies the Need for Positive-Pressure Ventilation and Determines the Effectiveness of Allocated Respiratory Therapy

BACKGROUND

Hospital-acquired pneumonia (HAP) and the need for positive-pressure ventilation (PPV) are significant postoperative pulmonary complications (PPCs) that increase patients' lengths of stay, mortality, and costs. Current tools used to predict PPCs use nonmodifiable preoperative factors; thus, they cannot assess provided respiratory therapy effectiveness. The Respiratory Assessment and Allocation of Therapy (RAAT) tool was created to identify HAP and the need for PPV and assist in assigning respiratory therapies. This study aimed to assess the RAAT tool's reliability and validity and determine if allocated respiratory procedures based on scores prevented HAP and the need for PPV.

METHODS

Electronic medical record data for nonintubated surgical ICU subjects scored with the RAAT tool were pulled from July 1, 2015-January 31, 2016, using a consecutive sampling technique. Sensitivity, specificity, and jackknife analysis were generated based on total RAAT scores. A unit-weighted analysis and mean differences of consecutive RAAT scores were analyzed with RAAT total scores ≥ 10 and the need for PPV.

RESULTS

The first or second RAAT score of ≤ 5 (unlikely to receive PPV) and ≥ 10 (likely to receive PPV) provided a sensitivity of 0.833 and 0.783 and specificity of 0.761 and 0.804, respectively. Jackknifed sensitivity and specificity for identified cutoffs above were 0.800-0.917 and 0.775-0.739 for the first RAAT score and 0.667-0.889 and 0.815-0.79 for the second RAAT score. The initial RAAT scores of ≥ 10 predicted the need for PPV (P < .001) and was associated with higher in-hospital mortality (P < .001). Mean differences between consecutive RAAT scores revealed decreasing scores did not need PPV.

CONCLUSIONS

The RAAT scoring tool demonstrated an association with the need for PPV using modifiable factors and appears to provide a quantitative method of determining if allocated respiratory therapy is effective.

What's Wrong in a Jump? Prediction and Validation of Splice Site Variants

Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research.

Length of Stay After Joint Arthroplasty is Less Than Predicted Using Two Risk Calculators

BACKGROUND

Predicting the length of stay (LOS) after total joint arthroplasty (TJA) has become more important with their recent removal from inpatient-only designation. The American College of Surgeons (ACS) National Surgical Quality Improvement Program surgical risk calculator and the CMS' diagnosis-related group (DRG) calculator are two common LOS predictors. The aim of our study was to determine how our actual LOS compared with those predicted by both the ACS and DRG.

METHODS

99 consecutive TJA (49 hips and 50 knee procedures) were reviewed in Medicare-eligible patients from four fellowship-trained arthroplasty surgeons. Predicted LOS was calculated using the DRG and ACS risk calculators for each patient using demographics, medical histories, and comorbidities. LOS was compared between the predicted and the actual LOS for both total hip arthroplasty (THA) and total knee arthroplasty (TKA) using paired t-tests.

RESULTS

Actual LOS was shorter in the THA group vs the TKA group (1.29 days vs 1.46 days, P < .05). The actual LOS of patients at our institution was significantly shorter than both DRG and ACS predictions for both THA and TKA (P < .05). In both the THA and TKA patients, the actual LOS (1.29 and 1.46 day) was significantly shorter than the DRG-predicted LOS (2.15 and 2.15 days) which was significantly shorter than the ACS-predicted LOS (2.9 and 3.14 days).

CONCLUSION

We found the actual LOS was significantly shorter than that predicted by both the DRG and ACS risk calculators. Current risk calculators may not be accurate for contemporary fast-track protocols and newer tools should be developed.

Clinical Prediction of Iron Deficiency at Age 2 Years: A National Cross-sectional Study in France

OBJECTIVE

To assess the diagnostic accuracy of existing clinical criteria and to develop prediction tools for iron deficiency in 2-year-old children.

STUDY DESIGN

In a national cross-sectional study conducted in primary care pediatricians' practices throughout France, 2-year-old children were consecutively included (2016-2017). Multivariable logistic regression modeling and bootstrapping were used to develop several clinical models to predict iron deficiency (serum ferritin <12 μg/L). These models used the best criteria and combinations among the American Academy of Pediatrics' (AAP) criteria adapted to the European context (n = 10), then all potential predictors (n = 19). One model was then simplified into a simple prediction tool.

RESULTS

Among 568 included infants, 38 had iron deficiency (6.7%). In univariable analyses, no significant association with iron deficiency was observed for 8 of the 10 adapted AAP criteria. Three criteria (both parents born outside the European Union, low weight at 1 year old, and weaning to cow's milk without supplemental iron) were retained in the AAP model, which area under the receiver operating characteristic curve, sensitivity, and specificity were 0.62 (95% CI, 0.58-0.67), 30% (95% CI, 22%-39%), and 95% (95% CI, 92%-97%), respectively. Four criteria were retained in a newly derived simple prediction tool (≥1 criterion among the 3 previous plus duration of iron-rich formula consumption <12 months), which area under the receiver operating characteristic curve, sensitivity, and specificity were 0.72 (95% CI, 0.65-0.79), 63% (95% CI, 47%-80%), and 81% (95% CI, 70%-91%), respectively.

CONCLUSIONS

All prediction tools achieved acceptable diagnostic accuracy. The newly derived simple prediction tool offered potential ease of use.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02484274.

miRNA Targets: From Prediction Tools to Experimental Validation

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.

Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner's hype cycle

Artificial Intelligence (AI) in general, and Machine Learning (ML)-based applications in particular, have the potential to change the scope of healthcare, including orthopaedic surgery.

The greatest benefit of ML is in its ability to learn from real-world clinical use and experience, and thereby its capability to improve its own performance.

Many successful applications are known in orthopaedics, but have yet to be adopted and evaluated for accuracy and efficacy in patients’ care and doctors’ workflows.

The recent hype around AI triggered hope for development of better risk stratification tools to personalize orthopaedics in all subsequent steps of care, from diagnosis to treatment.

Computer vision applications for fracture recognition show promising results to support decision-making, overcome bias, process high-volume workloads without fatigue, and hold the promise of even outperforming doctors in certain tasks.

In the near future, AI-derived applications are very likely to assist orthopaedic surgeons rather than replace us. ‘If the computer takes over the simple stuff, doctors will have more time again to practice the art of medicine’.⁷⁶

Cite this article: EFORT Open Rev 2020;5:593-603. DOI: 10.1302/2058-5241.5.190092

Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene

Single nucleotide variants (SNVs) occurring in a protein coding gene may disrupt its function in multiple ways. Predicting this disruption has been recognized as an important problem in bioinformatics research. Many tools, hereafter p-tools, have been designed to perform these predictions and many of them are now of common use in scientific research, even in clinical applications. This highlights the importance of understanding the semantics of their outputs. To shed light on this issue, two questions are formulated, (i) do p-tools provide similar predictions? (inner consistency), and (ii) are these predictions consistent with the literature? (outer consistency). To answer these, six p-tools are evaluated with exhaustive SNV datasets from the BRCA1 gene. Two indices, called Kall and Kstrong, are proposed to quantify the inner consistency of pairs of p-tools while the outer consistency is quantified by standard information retrieval metrics. While the inner consistency analysis reveals that most of the p-tools are not consistent with each other, the outer consistency analysis reveals they are characterized by a low prediction performance. Although this result highlights the need of improving the prediction performance of individual p-tools, the inner consistency results pave the way to the systematic design of truly diverse ensembles of p-tools that can overcome the limitations of individual members.

Prediction tools in congenital diaphragmatic hernia

Because congenital diaphragmatic hernia (CDH) is characterized by a spectrum of severity, risk stratification is an essential component of care. In both the prenatal and postnatal periods, accurate prediction of outcomes may inform clinical decision-making, care planning, and resource allocation. This review examines the history and utility of the most well-established risk prediction tools currently available, and provides recommendations for their optimal use in the management of CDH patients.

Développement de la version franco-canadienne du questionnaire Risk Assessment and Prediction Tool (RAPT) chez une population préhospitalière recourant à une arthroplastie de la hanche ou du genou

Purpose: The primary purpose is to translate and assess the transcultural validity of the RAPT - a pre-operation questionnaire that helps predict the clients' post-operation process - for the French-Canadian population requiring an arthroplasty of the hip or of the knee. The second purpose is to determine the standard error of measurements of the French-Canadian version of RAPT. Method: The transcultural translation and adaptation process of RAPT follows four steps: (1) initial translation, (2) retro-translation, (3) assessment of the questionnaire's clarity by patients, 4) assessment of the translation's transcultural validity. Furthermore, the RAPT standard error of measurements was calculated. Results: Participants were recruited at the Hull and Montfort hospitals. Twenty participants were recruited for step 3 and 83 participants for step 4. Results suggest that the RAPT and the French-Canadian translation (RAPT-FC) are comparable, with intraclass, intralanguage, interlanguage and temportal interlanguage correlation coefficents that varied from 0.858 to 0.988. The standard error of measurements is 0.8. Conclusions: The RAPT-FC tool is comparable to the original English version of the RAPT. Using this questionnaire could help in the planning of postoperative resources associated to hip and knee replacements within the French-Canadian population.

Characterization and identification of antimicrobial peptides with different functional activities

In recent years, antimicrobial peptides (AMPs) have become an emerging area of focus when developing therapeutics hot spot residues of proteins are dominant against infections. Importantly, AMPs are produced by virtually all known living organisms and are able to target a wide range of pathogenic microorganisms, including viruses, parasites, bacteria and fungi. Although several studies have proposed different machine learning methods to predict peptides as being AMPs, most do not consider the diversity of AMP activities. On this basis, we specifically investigated the sequence features of AMPs with a range of functional activities, including anti-parasitic, anti-viral, anti-cancer and anti-fungal activities and those that target mammals, Gram-positive and Gram-negative bacteria. A new scheme is proposed to systematically characterize and identify AMPs and their functional activities. The 1st stage of the proposed approach is to identify the AMPs, while the 2nd involves further characterization of their functional activities. Sequential forward selection was employed to extract potentially informative features that are possibly associated with the functional activities of the AMPs. These features include hydrophobicity, the normalized van der Waals volume, polarity, charge and solvent accessibility-all of which are essential attributes in classifying between AMPs and non-AMPs. The results revealed the 1st stage AMP classifier was able to achieve an area under the receiver operating characteristic curve (AUC) value of 0.9894. During the 2nd stage, we found pseudo amino acid composition to be an informative attribute when differentiating between AMPs in terms of their functional activities. The independent testing results demonstrated that the AUCs of the multi-class models were 0.7773, 0.9404, 0.8231, 0.8578, 0.8648, 0.8745 and 0.8672 for anti-parasitic, anti-viral, anti-cancer, anti-fungal AMPs and those that target mammals, Gram-positive and Gram-negative bacteria, respectively. The proposed scheme helps facilitate biological experiments related to the functional analysis of AMPs. Additionally, it was implemented as a user-friendly web server (AMPfun, http://fdblab.csie.ncu.edu.tw/AMPfun/index.html) that allows individuals to explore the antimicrobial functions of peptides of interest.

Identifying Older Patients at High Risk for Emergency Department Visits and Hospitalization

Hospitalizations are costly, potentially hazardous for older patients, and sometimes preventable. With Medicare's implementation of hospital penalties for 30-day readmissions on certain index conditions, health care organizations have prioritized addressing those issues that lead to avoidable hospitalizations. Little is known about the utility and feasibility of using standardized tools to identify adults at risk for hospitalizations in primary care. In this study, the goal was to determine, from a sample of 60 adults aged 65 and older, whether the Probability of Repeat Admission (PRA), the Vulnerable Elders Survey (VES-13), or a provider estimate of likelihood of hospitalization could identify patients at high risk for emergency department (ED) visits or hospitalization at 6 and 12 months, while being feasible to administer in a primary care setting. PRA, VES-13, and provider estimate were administered in an outpatient practice. Number of ED visits and hospitalizations at 6 and 12 months were assessed through follow-up phone calls and chart review. PRA and provider estimate were not significant predictors of hospitalizations at 6 months (PRA odds ratio [OR] 1.95; P = 0.39; physician estimate OR 4.33, P = 0.08), but were at 12 months (PRA OR 6.00; P < 0.001; physician estimate OR 2.3; P < 0.05). Additionally, a hospitalization during the prior year was not a significant predictor of hospitalization at 6 months (OR 2.97; P = 0.15) but was at 12 months (OR 3.89, P < 0.05). No tool was a significant predictor of ED visits at either time. PRA and the physician estimate were easy to administer and feasible to implement in a primary care setting.

Classes, Databases, and Prediction Methods of Pharmaceutically and Commercially Important Cystine-Stabilized Peptides

Cystine-stabilized peptides represent a large family of peptides characterized by high structural stability and bactericidal, fungicidal, or insecticidal properties. Found throughout a wide range of taxa, this broad and functionally important family can be subclassified into distinct groups dependent upon their number and type of cystine bonding patters, tertiary structures, and/or their species of origin. Furthermore, the annotation of proteins related to the cystine-stabilized family are under-represented in the literature due to their difficulty of isolation and identification. As a result, there are several recent attempts to collate them into data resources and build analytic tools for their dynamic prediction. Ultimately, the identification and delivery of new members of this family will lead to their growing inclusion into the repertoire of commercial viable alternatives to antibiotics and environmentally safe insecticides. This review of the literature and current state of cystine-stabilized peptide biology is aimed to better describe peptide subfamilies, identify databases and analytics resources associated with specific cystine-stabilized peptides, and highlight their current commercial success.

Tools for Sequence-Based miRNA Target Prediction: What to Choose?

MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists.

Factors to inform clinicians about the end of life in severe chronic obstructive pulmonary disease

CONTEXT

Palliative services have historically been offered to terminal patients with cancer, but much less so in other chronic illnesses such as chronic obstructive pulmonary disease (COPD) because of difficulties in predicting the trajectory to death.

OBJECTIVES

The goal of this study was to determine if the change over time of the key parameters (trajectory) in patients with severe COPD can independently predict short-term mortality.

METHODS

We analyzed data from 1218 patients with severe COPD. Multivariate models for trajectory change were used to forecast mortality at 12 months.

RESULTS

Changes in several variables by defined cutpoints increase significantly and independently the odds of dying in 12 months. The earliest and strongest predictors were the decrease in gait speed by 0.14 m/s or six-minute walk by 50 m (odds ratio [OR] 4.40, P<0.0001). Alternatively, if six-minute walk or gait speed were not used, change toward perceiving a very sedentary state using a single question (OR 3.56, P=0.0007) and decrease in maximal inspiratory pressure greater than 11 cmH2O (OR 2.19, P=0.0217) were predictive, followed by change toward feeling upset or downhearted (OR 2.44, P=0.0250), decrease in room air resting partial pressure of oxygen greater than 5 mmHg (OR 2.46, P=0.0156), and increase in room air resting partial pressure of carbon dioxide greater than 3 mmHg (OR 2.8, P=0.0039). Change over time models were more discriminative (higher c-statistics) than change from baseline models.

CONCLUSION

The changes in defined variables and patient-reported outcomes by defined cutpoints were independently associated with increased 12-month mortality in patients with severe COPD. These results may inform clinicians when to initiate end-of-life communications and palliative care.

Predictive and prognostic models in radical prostatectomy candidates: a critical analysis of the literature

CONTEXT

Numerous predictive and prognostic tools have recently been developed for risk stratification of prostate cancer (PCa) patients who are candidates for or have been treated with radical prostatectomy (RP).

OBJECTIVE

To critically review the currently available predictive and prognostic tools for RP patients and to describe the criteria that should be applied in selecting the most accurate and appropriate tool for a given clinical scenario.

EVIDENCE ACQUISITION

A review of the literature was performed using the Medline, Scopus, and Web of Science databases. Relevant reports published between 1996 and January 2010 identified using the keywords prostate cancer, radical prostatectomy, predictive tools, predictive models, and nomograms were critically reviewed and summarised.

EVIDENCE SYNTHESIS

We identified 16 predictive and 22 prognostic validated tools that address a variety of end points related to RP. The majority of tools are prediction models, while a few consist of risk-stratification schemes. Regardless of their format, the tools can be distinguished as preoperative or postoperative. Preoperative tools focus on either predicting pathologic tumour characteristics or assessing the probability of biochemical recurrence (BCR) after RP. Postoperative tools focus on cancer control outcomes (BCR, metastatic progression, PCa-specific mortality [PCSM], overall mortality). Finally, a novel category of tools focuses on functional outcomes. Prediction tools have shown better performance in outcome prediction than the opinions of expert clinicians. The use of these tools in clinical decision-making provides more accurate and highly reproducible estimates of the outcome of interest. Efforts are still needed to improve the available tools' accuracy and to provide more evidence to further justify their routine use in clinical practice. In addition, prediction tools should be externally validated in independent cohorts before they are applied to different patient populations.

CONCLUSIONS

Predictive and prognostic tools represent valuable aids that are meant to consistently and accurately provide most evidence-based estimates of the end points of interest. More accurate, flexible, and easily accessible tools are needed to simplify the practical task of prediction.

Minimally Invasive Therapy for Prostate Cancer: Use of Nomograms to Counsel Patients about the Choice and Probable Outcome of Therapy

Despite dramatic and recently accelerated advances in the reduction of morbidity linked to radical prostatectomies, significant short- and long-term morbidity is still associated with this surgical procedure. Currently both surgical and nonsurgical minimally invasive options are available for men with clinically localized prostate cancer, including laparoscopic and robotic radical prostatectomy, brachytherapy, and cryosurgical ablation of the prostate, with others, such as high intensity focused ultrasound, under investigation. In continued efforts to improve patient outcomes and to tailor treatment options to individual patient circumstances, nomograms have been developed and are increasingly being used by physicians and patients, alike, to guide therapeutic choices at each stage of disease. This tool predicts the possibility of successful treatment for the patient based on factors such as prostate-specific antigen levels, clinical stage of disease, and biopsy results. The current and future development, design, availability, and use of nomograms is described along with the historic and newer minimally invasive treatment options for prostate cancer.