Predicting venous thromboembolism in multiple myeloma: development and validation of the IMPEDE VTE score

Venous thromboembolism (VTE) is a common cause of morbidity and mortality among patients with multiple myeloma (MM). The International Myeloma Working Group (IMWG) developed guidelines recommending primary thromboprophylaxis, in those identified at high-risk of VTE by the presence of risk factors. The National Comprehensive Cancer Network (NCCN) has adopted these guidelines; however, they lack validation. We sought to develop and validate a risk prediction score for VTE in MM and to evaluate the performance of the current IMWG/NCCN guidelines. Using 4446 patients within the Veterans Administration Central Cancer Registry, we used time-to-event analyses to develop a risk score for VTE in patients with newly diagnosed MM starting chemotherapy. We externally validated the score using the Surveillance, Epidemiology, End Results (SEER)-Medicare database (N = 4256). After identifying independent predictors of VTE, we combined the variables to develop the IMPEDE VTE score (Immunomodulatory agent; Body Mass Index ≥25 kg/m2 ; Pelvic, hip or femur fracture; Erythropoietin stimulating agent; Dexamethasone/Doxorubicin; Asian Ethnicity/Race; VTE history; Tunneled line/central venous catheter; Existing thromboprophylaxis). The score showed satisfactory discrimination in the derivation cohort, c-statistic = 0.66. Risk of VTE significantly increased as score increased (hazard ratio 1.20, P = <.0001). Within the external validation cohort, IMPEDE VTE had a c-statistic of 0.64. For comparison, when evaluating the performance of the IMWG/NCCN guidelines, the c-statistic was 0.55. In summary, the IMPEDE VTE score outperformed the current IMWG/NCCN guidelines and could be considered as the new standard risk stratification for VTE in MM.

The REDS score: a new scoring system to risk-stratify emergency department suspected sepsis: a derivation and validation study

OBJECTIVE

To derive and validate a new clinical prediction rule to risk-stratify emergency department (ED) patients admitted with suspected sepsis.

DESIGN

Retrospective prognostic study of prospectively collected data.

SETTING

ED.

PARTICIPANTS

Patients aged ≥18 years who met two Systemic Inflammatory Response Syndrome criteria or one Red Flag sepsis criteria on arrival, received intravenous antibiotics for a suspected infection and admitted.

PRIMARY OUTCOME MEASURE

In-hospital all-cause mortality.

METHOD

The data were divided into derivation and validation cohorts. The simplified-Mortality in Severe Sepsis in the ED score and quick-SOFA scores, refractory hypotension and lactate were collectively termed 'component scores' and cumulatively termed the 'Risk-stratification of ED suspected Sepsis (REDS) score'. Each patient in the derivation cohort received a score (0-3) for each component score. The REDS score ranged from 0 to 12. The component scores were subject to univariate and multivariate logistic regression analyses. The receiver operator characteristic (ROC) curves for the REDS and the components scores were constructed and their cut-off points identified. Scores above the cut-off points were deemed high-risk. The area under the ROC (AUROC) curves and sensitivity for mortality of the high-risk category of the REDS score and component scores were compared. The REDS score was internally validated.

RESULTS

2115 patients of whom 282 (13.3%) died in hospital. Derivation cohort: 1078 patients with 140 deaths (13%). The AUROC curve with 95% CI, cut-off point and sensitivity for mortality (95% CI) of the high-risk category of the REDS score were: derivation: 0.78 (0.75 to 0.80); ≥3; 85.0 (78 to 90.5).

VALIDATION

0.74 (0.71 to 0.76); ≥3; 84.5 (77.5 to 90.0). The AUROC curve and the sensitivity for mortality of the REDS score was better than that of the component scores. Specificity and mortality rates for REDS scores of ≥3, ≥5 and ≥7 were 54.8%, 88.8% and 96.9% and 21.8%, 36.0% and 49.1%, respectively.

CONCLUSION

The REDS score is a simple and objective score to risk-stratify ED patients with suspected sepsis.

A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures

Background: Despite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20-40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to AD. Methods: The algorithm was developed using the open dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm. Results: The final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity. Conclusions: The algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.

Developing a clinical prediction rule to identify patients with lumbar disc herniation who demonstrate short-term improvement with mechanical lumbar traction

OBJECTIVE

To develop a clinical prediction rule (CPR) that predicts treatment responses to mechanical lumbar traction (MLT) among patients with lumbar disc herniation (LDH).

METHOD

This study was an uncontrolled prospective cohort study. The subjects included 103 patients diagnosed with LDH for which they underwent conservative therapy. The subjects received MLT for 2 weeks, and the application of any other medication was left at the discretion of the attending physician. The initial evaluation was performed prior to the initiation of treatment. The independent variables from the initial evaluation were imaging diagnosis, Oswestry Disability Index (ODI), Fear-Avoidance Beliefs Questionnaire score, visual analog scale, medical interview, physical examination. The patients whose ODI after 2 weeks of treatment improved by ≥50% of that at the initial evaluation were defined as responders.

RESULTS

Of the 103 subjects, 24 were responders, and the five predictors selected for the CPR were limited lumbar extension range of motion, low-level fear-avoidance beliefs regarding work, no segmental hypomobility in the lumbar spine, short duration of symptoms, and sudden onset of symptoms. For the patients with at least three of the five predictors, the probability of their ODI greatly improving increased from 23.3% to 48.7% compared with the patients without these predictors (positive likelihood ratio, 3.13).

CONCLUSION

Five factors were selected for the CPR to predict whether patients with LDH would demonstrate short-term improvement following conservative therapy with MLT.

A clinically-translatable machine learning algorithm for the prediction of Alzheimer's disease conversion: further evidence of its accuracy via a transfer learning approach

BACKGROUND

In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer's disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach.

METHODS

We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study.

RESULTS

Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705-0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706).

CONCLUSIONS

These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.

Utility of a clinical scoring system in prioritizing TB investigations - a systematic review

Introduction: Tuberculosis (TB) is among the 10 most common causes of death worldwide and it is the leading cause of mortality in people with human immunodeficiency virus (HIV). Clinical scoring systems have the potential to improve case finding and to prioritize patients for TB testing. Areas covered: This systematic review investigated the utility of prediction models to improve pulmonary tuberculosis (pTB) case finding. Studies were searched through PubMed until 15th of August 2018 and 20 studies were eligible according to the inclusion criteria. Data on study population, outcome measurements, predictors, and performance were extracted. Many studies showed promising results but lacked external validation. Furthermore, head-to-head studies are needed to compare the different prediction models. Sensitivities of the prediction models ranged from 26% to 96% and specificities from 18% to 92%, negative likelihood ratios (LR-) from 0.22 to 0.8 and positive likelihood ratios(LR+) 1.07 to 7.32. Composite scores including paraclinical measures added to sensitivity. Expert opinion: TB case finding is of utmost importance to advance the quest for global TB elimination, and simple measures to identify high-risk populations or persons to undergo further diagnostic evaluation are highly needed. A number of clinical scores are available and could be implemented in practice to improve case finding.

Delirium risk in non-surgical patients: systematic review of predictive tools

OBJECTIVE

Delirium is a common, serious condition associated with poor hospital outcomes. Guidelines recommend screening for delirium risk to target diagnostic and/or prevention strategies. This study critically reviews multicomponent delirium risk prediction tools in adult non-surgical inpatients.

STUDY DESIGN

Systematic review of studies incorporating at least two clinical factors in a multicomponent tool predicting risk of delirium during hospital admission. Derivation and validation studies were included. Study design, risk factors and tool performance were extracted and tabulated, and study quality was assessed by CHARMS criteria.

DATA SOURCES

PubMed, Embase, PsycINFO, and Cumulative Index to Nursing Health Literature (CINAHL) to 11^th March 2018.

DATA SYNTHESIS

22 derivation studies enrolling 38,874 participants (9 with a validation component) and 4 additional validation studies were identified, from a range of ward types. All studies had at least moderate risk of bias. Older age and cognitive, functional and sensory impairment were important predisposing factors. Precipitating risk factors included infection, illness severity, renal and electrolyte disturbances. Tools mostly did not differentiate between predisposing and precipitating risk factors mathematically or conceptually Most tools showed fair to good discrimination, and identified more than half of older inpatients at risk.

CONCLUSIONS

Several validated delirium risk prediction tools can identify patients at increased risk of delirium, but do not provide clear advice for clinical application. Most recommended cut-points are sensitive but have low specificity. Implementation studies demonstrating how risk screening can better direct clinical interventions in specific clinical settings are needed to define the potential value of these tools.

A Computer Application to Predict Adverse Events in the Short-Term Evolution of Patients With Exacerbation of Chronic Obstructive Pulmonary Disease

Background

Chronic obstructive pulmonary disease (COPD) is a common chronic disease. Exacerbations of COPD (eCOPD) contribute to the worsening of the disease and the patient’s evolution. There are some clinical prediction rules that may help to stratify patients with eCOPD by their risk of poor evolution or adverse events. The translation of these clinical prediction rules into computer applications would allow their implementation in clinical practice.

Objective

The goal of this study was to create a computer application to predict various outcomes related to adverse events of short-term evolution in eCOPD patients attending an emergency department (ED) based on valid and reliable clinical prediction rules.

Methods

A computer application, Prediction of Evolution of patients with eCOPD (PrEveCOPD), was created to predict 2 outcomes related to adverse events: (1) mortality during hospital admission or within a week after an ED visit and (2) admission to an intensive care unit (ICU) or an intermediate respiratory care unit (IRCU) during the eCOPD episode. The algorithms included in the computer tool were based on clinical prediction rules previously developed and validated within the Investigación en Resultados y Servicios de Salud COPD study. The app was developed for Windows and Android systems, using Visual Studio 2008 and Eclipse, respectively.

Results

The PrEveCOPD computer application implements the prediction models previously developed and validated for 2 relevant adverse events in the short-term evolution of patients with eCOPD. The application runs under Windows and Android systems and it can be used locally or remotely as a Web application. Full description of the clinical prediction rules as well as the original references is included on the screen. Input of the predictive variables is controlled for out-of-range and missing values. Language can be switched between English and Spanish. The application is available for downloading and installing on a computer, as a mobile app, or to be used remotely via internet.

Conclusions

The PrEveCOPD app shows how clinical prediction rules can be summarized into simple and easy to use tools, which allow for the estimation of the risk of short-term mortality and ICU or IRCU admission for patients with eCOPD. The app can be used on any computer device, including mobile phones or tablets, and it can guide the clinicians to a valid stratification of patients attending the ED with eCOPD.

Trial Registration

ClinicalTrials.gov NCT00102401; https://clinicaltrials.gov/ct2/show/results/NCT02434536 (Archived by WebCite at http://www.webcitation.org/76iwTxYuA)

International Registered Report Identifier (IRRID)

RR2-10.1186/1472-6963-11-322

ENDORECT: a preoperative score to accurately predict rectosigmoid involvement in patients with endometriosis

Study question

Could we construct and validate a preoperative score to predict rectosigmoid involvement in endometriosis (RE)?

Summary answer

We developed a simple preoperative score (ENDORECT) to predict RE.

What is known already

Accurate preoperative classification is important to optimize the surgical approach for patients with endometriosis but there is currently no reliable first-line examination to determine RE.

Study design size duration

This was a single-centre observational study including all women (N = 119) who underwent complete surgery for endometriosis between January 2011 and June 2016 in the Gynaecological Department of the University Hospital of Poissy Saint-Germain en Laye.

Participants/materials setting methods

Of the 119 women, 47 had RE and 72 did not. Two-thirds of the patients were randomly selected to derive the predictive score based on multiple logistic regression with internal validation by bootstrap. We used information from a self-assessment questionnaire, digital and speculum examination, transvaginal ultrasound and MRI. The score was then applied to the remaining sample of patients for validation.

Main results and the role of chance

Four variables were independently associated with RE: palpation of a posterior nodule on digital examination (aOR=5.6; 95%CI [1.7-21.8]); a UBESS score of 3 on ultrasonography (aOR=4.9; 95%CI [1.4-19.8); RE infiltration on MRI (aOR=6.8; 95%CI [2-25.5]); and presence of blood in the stools during menstruation (aOR=5.2; 95%CI [1.3-24.7]). The ROC-AUC of the model was 0.86 (95%CI [0.77-0.94]) and the bootstrap procedure showed that the model was stable. The ENDORECT score was derived from these four criteria and three risk groups were identified: the high-risk group (score>17) had a probability of RE of 100% with an specificity (Sp) of 100%, postive likelihood ratio (Lr+)>10; the intermediate-risk group (score: 7-17) had a probability of RE of 42%; and the low-risk group (score=0), with a sensitivity (Se) of 97%, negative likelihood ratio (Lr-) of 0.07 and a probability of RE of 5%. In the validation cohort, a score >17 predicted RE with an Sp of 96, Lr+ of 9.2, and probability of RE of 83%. Patients in this sample with a score=0, had an Se of 100%, Lr- of 0 and a probability of RE of 0%.

Limitations reasons for caution

The single-centre recruitment and over-representation of RE could constitute a referral bias.

Wider implications of the findings

The use of a preoperative predictive score could facilitate patient counselling and guide surgical management. Both MRI and transvaginal ultrasound provide independent information and are useful before surgery for RE.

Study funding/competing interests

No financial support was specifically received for this study. The authors declare no conflict of interest.

Trial registration number

N/A.

A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment

BACKGROUND

Available therapies for Alzheimer's disease (AD) can only alleviate and delay the advance of symptoms, with the greatest impact eventually achieved when provided at an early stage. Thus, early identification of which subjects at high risk, e.g., with MCI, will later develop AD is of key importance. Currently available machine learning algorithms achieve only limited predictive accuracy or they are based on expensive and hard-to-collect information.

OBJECTIVE

The current study aims to develop an algorithm for a 3-year prediction of conversion to AD in MCI and PreMCI subjects based only on non-invasively and effectively collectable predictors.

METHODS

A dataset of 123 MCI/PreMCI subjects was used to train different machine learning techniques. Baseline information regarding sociodemographic characteristics, clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy was used to extract 36 predictors. Leave-pair-out-cross-validation was employed as validation strategy and a recursive feature elimination procedure was applied to identify a relevant subset of predictors.

RESULTS

16 predictors were selected from all domains excluding sociodemographic information. The best model resulted a support vector machine with radial-basis function kernel (whole sample: AUC = 0.962, best balanced accuracy = 0.913; MCI sub-group alone: AUC = 0.914, best balanced accuracy = 0.874).

CONCLUSIONS

Our algorithm shows very high cross-validated performances that outperform the vast majority of the currently available algorithms, and all those which use only non-invasive and effectively assessable predictors. Further testing and optimization in independent samples will warrant its application in both clinical practice and clinical trials.

Development and prospective external validation of a tool to predict poor recovery at 9 months after acute ankle sprain in UK emergency departments: the SPRAINED prognostic model

OBJECTIVES

To develop and externally validate a prognostic model for poor recovery after ankle sprain.

SETTING AND PARTICIPANTS

Model development used secondary data analysis of 584 participants from a UK multicentre randomised clinical trial. External validation used data from 682 participants recruited in 10 UK emergency departments for a prospective observational cohort.

OUTCOME AND ANALYSIS

Poor recovery was defined as presence of pain, functional difficulty or lack of confidence in the ankle at 9 months after injury. Twenty-three baseline candidate predictors were included together in a multivariable logistic regression model to identify the best predictors of poor recovery. Relationships between continuous variables and the outcome were modelled using fractional polynomials. Regression parameters were combined over 50 imputed data sets using Rubin's rule. To minimise overfitting, regression coefficients were multiplied by a heuristic shrinkage factor and the intercept re-estimated. Incremental value of candidate predictors assessed at 4 weeks after injury was explored using decision curve analysis and the baseline model updated. The final models included predictors selected based on the Akaike information criterion (p<0.157). Model performance was assessed by calibration and discrimination.

RESULTS

Outcome rate was lower in the development (6.7%) than in the external validation data set (19.9%). Mean age (29.9 and 33.6 years), body mass index (BMI; 26.3 and 27.1 kg/m²), pain when resting (37.8 and 38.5 points) or bearing weight on the ankle (75.4 and 71.3 points) were similar in both data sets. Age, BMI, pain when resting, pain bearing weight, ability to bear weight, days from injury until assessment and injury recurrence were the selected predictors. The baseline model had fair discriminatory ability (C-statistic 0.72; 95% CI 0.66 to 0.79) but poor calibration. The updated model presented better discrimination (C-statistic 0.78; 95% CI 0.72 to 0.84), but equivalent calibration.

CONCLUSIONS

The models include predictors easy to assess clinically and show benefit when compared with not using any model.

TRIAL REGISTRATION NUMBER

ISRCTN12726986; Results.

Sex-specific performance of pre-imaging diagnostic algorithms for pulmonary embolism

Essentials Decision rules for pulmonary embolism are used indiscriminately despite possible sex-differences. Various pre-imaging diagnostic algorithms have been investigated in several prospective studies. When analysed at an individual patient data level the algorithms perform similarly in both sexes. Estrogen use and male sex were associated with a higher prevalence in suspected pulmonary embolism.

SUMMARY

Background In patients suspected of pulmonary embolism (PE), clinical decision rules are combined with D-dimer testing to rule out PE, avoiding the need for imaging in those at low risk. Despite sex differences in several aspects of the disease, including its diagnosis, these algorithms are used indiscriminately in women and men. Objectives To compare the performance, defined as efficiency and failure rate, of three pre-imaging diagnostic algorithms for PE between women and men: the Wells rule with fixed or with age-adjusted D-dimer cut-off, and a recently validated algorithm (YEARS). A secondary aim was to determine the sex-specific prevalence of PE. Methods Individual patient data were obtained from six studies using the Wells rule (fixed D-dimer, n = 5; age adjusted, n = 1) and from one study using the YEARS algorithm. All studies prospectively enrolled consecutive patients with suspected PE. Main outcomes were efficiency (proportion of patients in which the algorithm ruled out PE without imaging) and failure rate (proportion of patients with PE not detected by the algorithm). Outcomes were estimated using (multilevel) logistic regression models. Results The main outcomes showed no sex differences in any of the separate algorithms. With all three, the prevalence of PE was lower in women (OR, 0.66, 0.68 and 0.74). In women, estrogen use, adjusted for age, was associated with lower efficiency and higher prevalence and D-dimer levels. Conclusions The investigated pre-imaging diagnostic algorithms for patients suspected of PE show no sex differences in performance. Male sex and estrogen use are both associated with a higher probability of having the disease.

Development of a prediction tool for patients presenting with acute cough in primary care: a prognostic study spanning six European countries

BACKGROUND

Accurate prediction of the course of an acute cough episode could curb antibiotic overprescribing, but is still a major challenge in primary care.

AIM

The authors set out to develop a new prediction rule for poor outcome (re-consultation with new or worsened symptoms, or hospital admission) in adults presenting to primary care with acute cough.

DESIGN AND SETTING

Data were collected from 2604 adults presenting to primary care with acute cough or symptoms suggestive of lower respiratory tract infection (LRTI) within the Genomics to combat Resistance against Antibiotics in Community-acquired LRTI in Europe (GRACE; www.grace-lrti.org) Network of Excellence.

METHOD

Important signs and symptoms for the new prediction rule were found by combining random forest and logistic regression modelling. Performance to predict poor outcome in acute cough patients was compared with that of existing prediction rules, using the models' area under the receiver operator characteristic curve (AUC), and any improvement obtained by including additional test results (C-reactive protein [CRP], blood urea nitrogen [BUN], chest radiography, or aetiology) was evaluated using the same methodology.

RESULTS

The new prediction rule, included the baseline Risk of poor outcome, Interference with daily activities, number of years stopped Smoking (> or <45 years), severity of Sputum, presence of Crackles, and diastolic blood pressure (> or <85 mmHg) (RISSC85). Though performance of RISSC85 was moderate (sensitivity 62%, specificity 59%, positive predictive value 27%, negative predictive value 86%, AUC 0.63, 95% confidence interval [CI] = 0.61 to 0.67), it outperformed all existing prediction rules used today (highest AUC 0.53, 95% CI = 0.51 to 0.56), and could not be significantly improved by including additional test results (highest AUC 0.64, 95% CI = 0.62 to 0.68).

CONCLUSION

The new prediction rule outperforms all existing alternatives in predicting poor outcome in adult patients presenting to primary care with acute cough and could not be improved by including additional test results.

Development and validation of the HCV-MOSAIC risk score to assist testing for acute hepatitis C virus (HCV) infection in HIV-infected men who have sex with men (MSM)

Current guidelines recommend hepatitis C virus (HCV) testing for HIV-infected men who have sex with men (MSM) with ongoing risk behaviour, without specifying the type of risk behaviour. We developed and validated the HCV-MOSAIC risk score to assist HCV testing in HIV-infected MSM. The risk score consisted of six self-reported risk factors identified using multivariable logistic regression using data from the Dutch MOSAIC study (n = 213, 2009–2013). Area under the ROC curve (AUC), sensitivity, specificity, post-test-probability-of-disease and diagnostic gain were calculated. The risk score was validated in case–control studies from Belgium (n = 142, 2010–2013) and the United Kingdom (n = 190, 2003–2005) and in cross-sectional surveys at a Dutch sexually transmitted infections clinic (n = 284, 2007–2009). The AUC was 0.82; sensitivity 78.0% and specificity 78.6%. In the validation studies sensitivity ranged from 73.1% to 100% and specificity from 56.2% to 65.6%. The post-test-probability-of-disease ranged from 5.9% to 20.0% given acute HCV prevalence of 1.7% to 6.4%, yielding a diagnostic gain of 4.2% to 13.6%. The HCV-MOSAIC risk score can successfully identify HIV-infected MSM at risk for acute HCV infection. It could be a promising tool to improve HCV testing strategies in various settings.

Development of a clinical prediction model for the postthrombotic syndrome in a prospective cohort of patients with proximal deep vein thrombosis

Essentials We developed a prediction model for postthrombotic syndrome (PTS) after deep vein thrombosis (DVT). High risk predictors were iliac vein DVT, BMI>35 and moderate-severe Villalta category. Patients with a score ≥4 had an odds ratio of 5.9 (95% CI 2.1-16.6) for PTS. SOX-PTS score may select DVT patients for close monitoring or aggressive strategies to treat DVT.

SUMMARY

Background Postthrombotic syndrome (PTS) is a chronic complication that develops in 20-50% of patients after deep vein thrombosis (DVT). Although individual risk factors for PTS have been characterized, the ability to predict which DVT patients are likely to develop PTS remains limited. Objective To develop a clinical prediction score for PTS in patients with DVT. Methods The derivation cohort consisted of participants in the SOX Trial, a randomized double-blind placebo-controlled trial of elastic compression stockings versus placebo stockings worn for 2 years after DVT to prevent PTS in patients with a first proximal DVT, enrolled in 24 community and tertiary-care hospitals from 2004 to 2010. Multivariable logistic regression analysis of baseline characteristics was performed. The outcome was the occurrence of PTS, diagnosed starting from 6 months or later according to Ginsberg's criteria. Results Seven hundred and sixty-two patients were included in the analysis. The median follow-up was 728 days. The model includes three independent predictors, and has a range of possible scores from 0 to 5. High-risk predictors were: index DVT in the iliac vein; body mass index of ≥ 35 kg m-2 ; and moderate-severe Villalta severity category at DVT diagnosis. As compared with patients with a score of 0, those with a score of ≥ 4 had an odds ratio of 5.9 (95% confidence interval 2.1-16.6) for developing PTS. Conclusions To our knowledge, this is the first clinical prediction score for PTS. We identified three independent predictors that, when combined, predicted PTS risk after a first proximal DVT. The SOX-PTS score requires external validation before it can be considered for clinical use.

Identifying patients at risk of inhospital death or hospice transfer for early goals of care discussions in a US referral center: the HELPS model derived from retrospective data

OBJECTIVE

Create a score to identify patients at risk of death or hospice placement who may benefit from goals of care discussion earlier in the hospitalisation.

DESIGN

Retrospective cohort study to develop a risk index using multivariable logistic regression.

SETTING

Two tertiary care hospitals in Southeastern Minnesota.

PARTICIPANTS

92 879 adult general care admissions (50% male, average age 60 years).

PRIMARY AND SECONDARY OUTCOME MEASURES

Our outcome measure was an aggregate of inhospital death or discharge to hospice. Predictor variables for the model encompassed comorbidities, nutrition status, functional status, demographics, fall risk, mental status, Charlson Comorbidity Index and acuity of illness on admission. Resuscitation status, race, geographic area of residence and marital status were added as covariates to account for confounding.

RESULTS

Inhospital mortality and discharge to hospice were rare, with incidences of 1.2% and 0.8%, respectively. The Hospital End-of-Life Prognostic Score (HELPS) demonstrated good discrimination (C-statistic=0.866 in derivation set and 0.834 in validation set). The patients with the highest 5% of scores had an 8% risk of the outcome measure, relative risk 12.9 (10.9-15.4) when compared to the bottom 95%.

CONCLUSIONS

HELPS is able to identify patients with a high risk of inhospital death or need for hospice at discharge. These patients may benefit from early goals of care discussions.

Validation of a multivariate clinical prediction model for the diagnosis of mild stroke/transient ischemic attack in physician first-contact patient settings

We validate our previously developed (DOI: 10.1101/089227) clinical prediction rule for diagnosing transient ischemic attack on the basis of presenting clinical symptoms and compare its performance with the ABCD2 score in first-contact patient settings. Two independent and prospectively collected patient validation cohorts were used: (a) referral cohort-prospectively referred emergency department and general practitioner patients (N = 877); and (b) SpecTRA cohort-participants recruited as part of the SpecTRA biomarker project (N = 545). Outcome measure consisted of imaging-confirmed clinical diagnosis of mild stroke/transient ischemic attack. Results showed that our clinical prediction rule demonstrated significantly higher accuracy than the ABCD2 score for both the referral cohort (70.5% vs 59.0%; p < 0.001) and SpecTRA cohort (72.8% vs 68.3%; p = 0.028). We discuss the potential of our clinical prediction rule to replace the use of the ABCD2 score in the triage of transient ischemic attack clinic referrals.

Validation of a multivariate clinical prediction model for the diagnosis of mild stroke/transient ischemic attack in physician first-contact patient settings

We validate our previously developed (DOI: 10.1101/089227) clinical prediction rule for diagnosing transient ischemic attack on the basis of presenting clinical symptoms and compare its performance with the ABCD2 score in first-contact patient settings. Two independent and prospectively collected patient validation cohorts were used: (a) referral cohort-prospectively referred emergency department and general practitioner patients (N = 877); and (b) SpecTRA cohort-participants recruited as part of the SpecTRA biomarker project (N = 545). Outcome measure consisted of imaging-confirmed clinical diagnosis of mild stroke/transient ischemic attack. Results showed that our clinical prediction rule demonstrated significantly higher accuracy than the ABCD2 score for both the referral cohort (70.5% vs 59.0%; p < 0.001) and SpecTRA cohort (72.8% vs 68.3%; p = 0.028). We discuss the potential of our clinical prediction rule to replace the use of the ABCD2 score in the triage of transient ischemic attack clinic referrals.

Clinical Factors That Predict a Second ACL Injury After ACL Reconstruction and Return to Sport: Preliminary Development of a Clinical Decision Algorithm

Background

Biomechanical predictors of a second anterior cruciate ligament (ACL) injury after ACL reconstruction (ACLR) and return to sport (RTS) have been identified; however, these measures may not be feasible in a standard clinical environment.

Purpose/Hypothesis

The purpose of this study was to evaluate whether standard clinical measures predicted the risk of second ACL injuries. The hypothesis tested was that a combination of strength, function, and patient-reported measures at the time of RTS would predict the risk of second ACL injuries with high sensitivity and specificity.

Study Design

Case-control study; Level of evidence, 3 and Cohort study (prognosis); Level of evidence, 1.

Methods

A total of 163 participants (mean age, 16.7 ± 3.0 years) who underwent primary ACLR and were able to RTS were evaluated. All participants completed an assessment of isokinetic strength, hop testing, balance, and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Participants were tracked for a minimum of 24 months to identify occurrences of a second ACL injury. The initial 120 participants enrolled were used to develop a clinical prediction model that utilized classification and regression tree (CART) analysis, and the remaining 43 participants enrolled were used as a validation dataset. Additional analyses were performed in all 163 participants using Kaplan-Meier analysis and Cox proportional hazards modeling.

Results

Approximately 20% (23/114) of the initial subset of the cohort suffered a second ACL injury. CART analysis identified age, sex, knee-related confidence, and performance on the triple hop for distance at the time of RTS as the primary predictors of a second ACL injury. Using these variables, a model was generated from which high-risk (n = 53) and low-risk groups (n = 61) were identified. A total of 22 participants in the high-risk group and 1 participant in the low-risk group suffered a second ACL injury. High-risk participants fit 1 of 2 profiles: (1) age <19 years, triple hop for distance between 1.34 and 1.90 times body height, and triple hop for distance limb symmetry index (LSI) <98.5% (n = 43) or (2) age <19 years, triple hop for distance >1.34 times body height, triple hop for distance LSI >98.5%, female sex, and high knee-related confidence (n = 10). The validation step identified the high-risk group as being 5 times (odds ratio, 5.14 [95% CI, 1.00-26.46]) more likely to suffer a second ACL injury, with a sensitivity of 66.7% and specificity of 72.0%.

Conclusion

These findings recognize measures that accurately identify young patients at high risk of sustaining a second ACL injury within 24 months after RTS. The development of a clinical decision algorithm to identify high-risk patients, inclusive of clinically feasible variables such as age, sex, confidence, and performance on the triple hop for distance, can serve as a foundation to re-evaluate appropriate discharge criteria for RTS.

Performance of wells score to predict deep vein thrombosis and pulmonary embolism in endurance athletes

INTRODUCTION

There are an increasing number of reports describing deep vein thrombosis (DVT) and/or pulmonary embolism (PE) in otherwise healthy endurance athletes. The Wells score is the most commonly used clinical prediction rule to diagnose DVT/PE in clinical populations. However, the Wells score may have limited utility for recognition of DVT/PE in athletes, contributing to missed or delayed diagnosis.

OBJECTIVE

We performed an analysis of the ability of the Wells score to identify DVT/PE events in athletes through a review of published case reports.

METHODS

A systematic search of the literature yielded 11 case reports.

RESULTS

The Wells score had a 100% failure rate in identifying athletes with DVT (0/6) and PE (0/5), resulting in a delayed diagnosis for DVT of 20 ± 14 days. Retrospectively removing 'differential diagnosis' from the clinical prediction rule for DVT changed the Wells score median from 0 (range: -1 to 0) to 2 (range: 1 to 2); the threshold for predicting DVT as 'likely'. There were limited clinical characteristics captured in the Wells score for PE that were applicable to athletes, highlighting the need for reappraisal. Although the Wells score failed to accurately triage athletes with known DVT and/or PE, the addition of a D-dimer value (mean: 1566 ± 758ng/dL) to the Wells score correctly identified 9/9 athletes.

CONCLUSIONS

The Wells score had a 100% failure rate for triaging athletes with known DVT/PE. When performed, D-dimer adequately facilitated the additional diagnostic testing required for a timely diagnosis of DVT/PE in athletes. Improving awareness of an atypical presentation of thrombotic events in athletes may reduce the widespread underestimation of DVT/PE among athletes and facilitate the additional testing required for a timely diagnosis.

Safety of multidetector computed tomography pulmonary angiography to exclude pulmonary embolism in patients with a likely pretest clinical probability

Essentials Safety of computed tomography (CTPA) to exclude pulmonary embolism (PE) in all patients is debated. We analysed the outcome of PE-likely outpatients left untreated after negative CTPA alone. The 3-month venous thromboembolic risk in these patients was very low (0.6%; 95% CI 0.2-2.3). Multidetector CTPA alone safely excludes PE in patients with likely clinical probability.

SUMMARY

Background In patients with suspected pulmonary embolism (PE) classified as having a likely or high pretest clinical probability, the need to perform additional testing after a negative multidetector computed tomography pulmonary angiography (CTPA) finding remains a matter of debate. Objectives To assess the safety of excluding PE by CTPA without additional imaging in patients with a likely pretest probability of PE. Patients/Methods We retrospectively analyzed patients included in two multicenter management outcome studies that assessed diagnostic algorithms for PE diagnosis. Results Two thousand five hundred and twenty-two outpatients with suspected PE were available for analysis. Of these 2522 patients, 845 had a likely clinical probability as assessed by use of the simplified revised Geneva score. Of all of these patients, 314 had the diagnosis of PE excluded by a negative CTPA finding alone without additional testing, and were left without anticoagulant treatment and followed up for 3 months. Two patients presented with a venous thromboembolism (VTE) during follow-up. Therefore, the 3-month VTE risk in likely-probability patients after a negative CTPA finding alone was 2/314 (0.6%; 95% confidence interval [CI] 0.2-2.3%). Conclusions In outpatients with suspected PE and a likely clinical probability as assessed by use of the simplified revised Geneva score, CTPA alone seems to be able to safely exclude PE, with a low 3-month VTE rate, which is similar to the VTE rate following the gold standard, i.e. pulmonary angiography.

The modified respiratory index score (RIS) guides resource allocation in acute bronchiolitis

BACKGROUND AND OBJECTIVE

Bronchiolitis is a common disease in early childhood with increasing healthcare utilization. We aim to study how well a simple and improved respiratory score (the modified Respiratory Index Score [RIS]) would perform when predicting for a warranted admission.

METHODS

This is an observational prospective study, from June 2015 to December 2015 in a paediatric emergency department (ED) of a large tertiary hospital in Singapore. We included children aged less than 2 years old, presenting with typical symptoms and signs of bronchiolitis but excluded children with four or more previous wheezes, a gestation of <35 weeks, and known cardiopulmonary disease. We also performed a sensitivity analysis for children presenting with their first wheeze. We defined a warranted admission as a composite of: The need for airway intervention, intravenous hydration, and a hospital stay of 2 days or more.

RESULTS

Among 1,818 patients, the median age was 10.8 months (IQR 7.2-15.9). The median modified RIS score was 4.0 (IQR 3.0-5.0). A total of 19 (1.0%) children required respiratory support, 101 (5.6%) received intravenous hydration, and 571 (31.4%) required a hospital stay of 2 days or more. After adjusting for age and duration of illness, a modified RIS score of >4 predicted significantly for a warranted admission (adjusted Odds Ratio: 3.28, 95% confidence interval: 2.62-4.12). The association remained significant among children presenting with their first wheeze.

CONCLUSIONS

This simple respiratory tool predicts for the need for respiratory support, intravenous hydration, and a significant hospital stay of 2 days or more. Pediatr Pulmonol. 2017; 52:954-961. © 2017 Wiley Periodicals, Inc.

Predictive performance of the CHA2DS2-VASc rule in atrial fibrillation: a systematic review and meta-analysis

Essentials The widely recommended CHA2DS2-VASc shows conflicting results in contemporary validation studies. We performed a systematic review and meta-analysis of 19 studies validating CHA2DS2-VASc. There was high heterogeneity in stroke risks for different CHA2DS2-VASc scores. This was not explained by differences between setting of care, or by performing meta-regression.

SUMMARY

Background The CHA2DS2-VASc decision rule is widely recommended for estimating stroke risk in patients with atrial fibrillation (AF), although validation studies show ambiguous and conflicting results. Objectives To: (i) review existing studies validating CHA2DS2-VASc in AF patients who are not (yet) anticoagulated; (ii) meta-analyze estimates of stroke risk per score; and (iii) explore sources of heterogeneity across the validation studies. Methods We performed a systematic literature review and random effects meta-analysis of studies externally validating CHA2DS2-VASc in AF patients not receiving anticoagulants. To explore between-study heterogeneity in stroke risk, we stratified studies to the clinical setting in which patient enrollment started, and performed meta-regression. Results In total, 19 studies were evaluated, with over two million person-years of follow-up. In studies recruiting AF patients in hospitals, stroke risks for scores of 0, 1 and 2 were 0.4% (approximate 95% prediction interval [PI] 0.2-3.2%), 1.2% (95% PI 0.1-3.8%), and 2.2% (95% PI 0.03-7.8%), respectively. These were consistently higher than those in studies recruiting patients from the open general population, with risks of 0.2% (95% PI 0.0-0.9%), 0.7% (95% PI 0.3-1.2%) and 1.5% (95% PI 0.4-3.3%) for scores of 0, 1, and 2, respectively. Heterogeneity, as reflected by the wide PIs, could not be fully explained by meta-regression. Conclusions Studies validating CHA2DS2-VASc show high heterogeneity in predicted stroke risks for different scores.

Using Flow Characteristics in Three-Dimensional Power Doppler Ultrasound Imaging to Predict Complete Responses in Patients Undergoing Neoadjuvant Chemotherapy

OBJECTIVES

Strategies are needed for the identification of a poor response to treatment and determination of appropriate chemotherapy strategies for patients in the early stages of neoadjuvant chemotherapy for breast cancer. We hypothesize that power Doppler ultrasound imaging can provide useful information on predicting response to neoadjuvant chemotherapy.

METHODS

The solid directional flow of vessels in breast tumors was used as a marker of pathologic complete responses (pCR) in patients undergoing neoadjuvant chemotherapy. Thirty-one breast cancer patients who received neoadjuvant chemotherapy and had tumors of 2 to 5 cm were recruited. Three-dimensional power Doppler ultrasound with high-definition flow imaging technology was used to acquire the indices of tumor blood flow/volume, and the chemotherapy response prediction was established, followed by support vector machine classification.

RESULTS

The accuracy of pCR prediction before the first chemotherapy treatment was 83.87% (area under the ROC curve [AUC] = 0.6957). After the second chemotherapy treatment, the accuracy of was 87.9% (AUC = 0.756). Trend analysis showed that good and poor responders exhibited different trends in vascular flow during chemotherapy.

CONCLUSIONS

This preliminary study demonstrates the feasibility of using the vascular flow in breast tumors to predict chemotherapeutic efficacy.

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm

BACKGROUND

Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers.

AIM

To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care.

DESIGN AND SETTING

Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months.

METHOD

Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT.

RESULTS

From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9.

CONCLUSION

The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking.

Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System

Background

Chronic obstructive pulmonary disease (COPD) is a progressive, chronic respiratory disease with a significant socioeconomic burden. Exacerbations, the sudden and sustained worsening of symptoms, can lead to hospitalization and reduce quality of life. Major limitations of previous telemonitoring interventions for COPD include low compliance, lack of consensus on what constitutes an exacerbation, limited numbers of patients, and short monitoring periods. We developed a telemonitoring system based on a digital health platform that was used to collect data from the 1-year EDGE (Self Management and Support Programme) COPD clinical trial aiming at daily monitoring in a heterogeneous group of patients with moderate to severe COPD.

Objective

The objectives of the study were as follows: first, to develop a systematic and reproducible approach to exacerbation identification and to track the progression of patient condition during remote monitoring; and second, to develop a robust algorithm able to predict COPD exacerbation, based on vital signs acquired from a pulse oximeter.

Methods

We used data from 110 patients, with a combined monitoring period of more than 35,000 days. We propose a finite-state machine–based approach for modeling COPD exacerbation to gain a deeper insight into COPD patient condition during home monitoring to take account of the time course of symptoms. A robust algorithm based on short-period trend analysis and logistic regression using vital signs derived from a pulse oximeter is also developed to predict exacerbations.

Results

On the basis of 27,260 sessions recorded during the clinical trial (average usage of 5.3 times per week for 12 months), there were 361 exacerbation events. There was considerable variation in the length of exacerbation events, with a mean length of 8.8 days. The mean value of oxygen saturation was lower, and both the pulse rate and respiratory rate were higher before an impending exacerbation episode, compared with stable periods. On the basis of the classifier developed in this work, prediction of COPD exacerbation episodes with 60%-80% sensitivity will result in 68%-36% specificity.

Conclusions

All 3 vital signs acquired from a pulse oximeter (pulse rate, oxygen saturation, and respiratory rate) are predictive of COPD exacerbation events, with oxygen saturation being the most predictive, followed by respiratory rate and pulse rate. Combination of these vital signs with a robust algorithm based on machine learning leads to further improvement in positive predictive accuracy.

Trial Registration

International Standard Randomized Controlled Trial Number (ISRCTN): 40367841; http://www.isrctn.com/ISRCTN40367841 (Archived by WebCite at http://www.webcitation.org/6olpMWNpc)

Adverse outcomes in older adults attending emergency departments: a systematic review and meta-analysis of the Identification of Seniors At Risk (ISAR) screening tool

Background

older adults are frequent users of emergency services and demonstrate high rates of adverse outcomes following emergency care.

Objective

to perform a systematic review and meta-analysis of the Identification of Seniors At Risk (ISAR) screening tool, to determine its predictive value in identifying adults ≥65 years at risk of functional decline, unplanned emergency department (ED) readmission, emergency hospitalisation or death within 180 days after index ED visit/hospitalisation.

Methods

a systematic literature search was conducted in PubMed, EMBASE, CINAHL, EBSCO and the Cochrane Library to identify validation and impact analysis studies of the ISAR tool. A pre-specified ISAR score of ≥2 (maximum score 6 points) was used to identify patients at high risk of adverse outcomes. A bivariate random effects model generated pooled estimates of sensitivity and specificity. Statistical heterogeneity was explored and methodological quality was assessed using validated criteria.

Results

thirty-two validation studies (n = 12,939) are included. At ≥2, the pooled sensitivity of the ISAR for predicting ED return, emergency hospitalisation and mortality at 6 months is 0.80 (95% confidence interval (CI) 0.70-0.87), 0.82 (95% CI 0.74-0.88) and 0.87 (95% CI 0.75-0.94), respectively, with a pooled specificity of 0.31 (95% CI 0.24-0.38), 0.32 (95% CI 0.24-0.41) and 0.35 (95% CI 0.26-0.44). Similar values are demonstrated at 30 and 90 days. Three heterogeneous impact analysis studies examined the clinical implementation of the ISAR and reported mixed findings across patient and process outcomes.

Conclusion

the ISAR has modest predictive accuracy and may serve as a decision-making adjunct when determining which older adults can be safely discharged.

The Cape Town Clinical Decision Rule for Streptococcal Pharyngitis in Children

BACKGROUND

Existing clinical decision rules (CDRs) to diagnose group A streptococcal (GAS) pharyngitis have not been validated in sub-Saharan Africa. We developed a locally applicable CDR while evaluating existing CDRs for diagnosing GAS pharyngitis in South African children.

METHODS

We conducted a prospective cohort study and enrolled 997 children 3-15 years of age presenting to primary care clinics with a complaint of sore throat, and whose parents provided consent. Main outcome measures were signs and symptoms of pharyngitis and a positive GAS culture from a throat swab. Bivariate and multivariate analyses were used to develop the CDR. In addition, the diagnostic effectiveness of 6 existing rules for predicting a positive culture in our cohort was assessed.

RESULTS

A total of 206 of 982 children (21%) had a positive GAS culture. Tonsillar swelling, tonsillar exudates, tender or enlarged anterior cervical lymph nodes, absence of cough and absence of rhinorrhea were associated with positive cultures in bivariate and multivariate analyses. Four variables (tonsillar swelling and one of tonsillar exudate, no rhinorrhea, no cough), when used in a cumulative score, showed 83.7% sensitivity and 32.2% specificity for GAS pharyngitis. Of existing rules tested, the rule by McIsaac et al had the highest positive predictive value (28%), but missed 49% of the culture-positive children who should have been treated.

CONCLUSION

The new 4-variable CDR for GAS pharyngitis (ie, tonsillar swelling and one of tonsillar exudate, no rhinorrhea, no cough) outperformed existing rules for GAS pharyngitis diagnosis in children with symptomatic sore throat in Cape Town.

Improving Diagnostic Accuracy and Efficiency of Suspected Bone Stress Injuries

CONTEXT

Lower extremity stress fractures among athletes and military recruits cause significant morbidity, fiscal costs, and time lost from sport or training. During fiscal years (FY) 2012 to 2014, 1218 US Air Force trainees at Joint Base San Antonio-Lackland, Texas, were diagnosed with stress fracture(s). Diagnosis relied heavily on bone scans, often very early in clinical course and often in preference to magnetic resonance imaging (MRI), highlighting the need for an evidence-based algorithm for stress injury diagnosis and initial management.

EVIDENCE ACQUISITION

To guide creation of an evidence-based algorithm, a literature review was conducted followed by analysis of local data. Relevant articles published between 1995 and 2015 were identified and reviewed on PubMed using search terms stress fracture, stress injury, stress fracture imaging, and stress fracture treatment. Subsequently, charts were reviewed for all Air Force trainees diagnosed with 1 or more stress injury in their outpatient medical record in FY 2014.

STUDY DESIGN

Clinical review.

LEVEL OF EVIDENCE

Level 4.

RESULTS

In FY 2014, 414 trainees received a bone scan and an eventual diagnosis of stress fracture. Of these scans, 66.4% demonstrated a stress fracture in the symptomatic location only, 21.0% revealed stress fractures in both symptomatic and asymptomatic locations, and 5.8% were negative in the symptomatic location but did reveal stress fracture(s) in asymptomatic locations. Twenty-one percent (18/85) of MRIs performed a mean 6 days (range, 0- 21 days) after a positive bone scan did not demonstrate any stress fracture.

CONCLUSION

Bone stress injuries in military training environments are common, costly, and challenging to diagnose. MRI should be the imaging study of choice, after plain radiography, in those individuals meeting criteria for further workup.

Unanticipated Nocturnal Oxygen Requirement during Positive Pressure Therapy for Sleep Apnea and Medical Comorbidities

STUDY OBJECTIVES

Home-based management of sleep-disordered breathing (SDB) generally excludes patients with significant medical comorbidities, but such an approach lacks scientific evidence. The current study examined whether significant medical comorbidities are associated with persistent hypoxia that requires unanticipated nocturnal O₂ supplementation to positive airway pressure (PAP) therapy. Conceivably, in such patients, home-based management of SDB may not detect or therefore adequately treat persistent hypoxia.

METHODS

In this retrospective study of 200 patients undergoing laboratory-based polysomnography, we ascertained significant medical comorbidities (chronic obstructive pulmonary disease, congestive heart failure, and morbid obesity) and their association with the need for unanticipated O₂ supplementation to PAP therapy. Postural oxygen (SpO₂) desaturations between upright and reclining positions were determined during calm wakefulness.

RESULTS

Postural change in SpO₂ during calm wakefulness was greater in patients who eventually needed nocturnal O₂ supplementation to PAP therapy than those needing PAP therapy alone (p < 0.0001). The presence of chronic obstructive pulmonary disease (odds ratio [OR] 6.0; 95% confidence interval [CI]; 2.1, 17.5; p = 0.001), morbid obesity (OR 3.6; 95% CI 1.9, 7.0; p < 0.0001), and age older than 50 y (OR 2.8; 95% CI 1.3, 5.9; p = 0.007) but not heart failure were associated with unanticipated need for nocturnal O₂ supplementation. A clinical prediction rule of less than two determinants (age older than 50 y, morbid obesity, chronic obstructive pulmonary disease, and postural SpO₂ desaturation greater than 5%) had excellent negative predictive value (0.92; 95% CI 0.85, 0.96) and likelihood ratio of negative test (0.08; 95% CI 0.04, 0.16).

CONCLUSIONS

Medical comorbidities can predict persistent hypoxia that requires unanticipated O₂ supplementation to PAP therapy. Such findings justify the use of medical comorbidities to exclude home management of SDB.

COMMENTARY

A commentary on this article appears in this issue on page 7.

Predicting 5-Year Risk of RRT in Stage 3 or 4 CKD: Development and External Validation

BACKGROUND AND OBJECTIVES

Only a minority of patients with CKD progress to renal failure. Despite the potential benefits of risk stratification in the CKD population, risk prediction models are not routinely used. Our objective was to develop and externally validate a clinically useful and pragmatic prediction model for the 5-year risk of progression to RRT in stage 3 or 4 CKD.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We used a retrospective cohort design. The development cohort consisted of 22,460 Kaiser Permanente Northwest members with stage 3 or 4 CKD (baseline 2002-2008). The validation cohort consisted of 16,553 Kaiser Permanente Colorado members with stage 3-4 CKD (baseline 2006-2008). The final model included eight predictors: age, sex, eGFR, hemoglobin, proteinuria/albuminuria, systolic BP, antihypertensive medication use, and diabetes and its complications.

RESULTS

In the Northwest and Colorado cohorts, there were 737 and 360 events, and observed 5-year Kaplan-Meier risks of 4.72% (95% confidence interval [95% CI], 4.38 to 5.06) and 2.57% (95% CI, 2.30 to 2.83), respectively. Our prediction model performed extremely well in the development cohort, with a c-statistic of 0.96, an R2 of 79.7%, and good calibration. We had similarly good performance in the external validation cohort, with a c-statistic of 0.95, R2 of 81.2%, and good calibration. In the external validation cohort, the observed risk was slightly lower than the predicted risk in the highest-risk quintile. Using the top quintile of predicted risk as a cutpoint gave a sensitivity of 92.2%.

CONCLUSIONS

We developed a pragmatic prediction model and risk score for predicting the 5-year RRT risk in stage 3 and 4 CKD. This model uses variables that are typically available in routine primary care settings, and can be used to help guide important decisions such as timing of referral to nephrology and fistula placement.

Minor Head Trauma in the Pediatric Emergency Department: Decision Making Nodes

BACKGROUND

Minor head trauma is one of the leading causes of accessing pediatric emergency departments; however, only a limited number of patients develops clinically relevant brain injuries.

OBJECTIVES

The aim of this review is to provide physicians a clinical pathway for managing pediatric minor head trauma.

METHODS

A Pubmed/Medline search was conducted through the following entries: "minor head trauma", "mild head trauma", "minor head injury", "mild head injury" or "acute head trauma". All the studies including pediatric samples between 2000 and 2015 were considered for a critical review. A few articles written before 2000 were analyzed for their relevance.

RESULTS

The Pediatric Emergency Care Applied Research Network (PECARN) algorithm identified children with a very low risk for clinically relevant brain injuries (normal mental status, no loss of consciousness, no vomiting, non-severe injury mechanism, no signs of basilar skull fracture, no severe headache, no evident clinical worsening over time and no multiple symptoms) and offered the only validated clinical prediction rule to select candidates for CT scans. Other proposed clinical prediction rules (including NEXUS II, CHALICE and CATCH), that were not validated, have a lower sensitivity than PECARN algorithm. Skull X-ray, cerebral magnetic resonance and cranial ultrasonography could provide useful information in selected cases.

CONCLUSIONS

The critical use of PECARN rule represents the best validated clinical tool for the early identification of children with a clinically relevant brain injury. Its application should be integrated with physician experience and judgement, parental compliance and clinical observation.

External Validation of Risk Prediction Scores for Invasive Candidiasis in a Medical/Surgical Intensive Care Unit: An Observational Study

Background:

The aim of this study was to conduct external validation of risk prediction scores for invasive candidiasis.

Methods:

We conducted a prospective observational study in a 12-bedded adult medical/surgical Intensive Care Unit (ICU) to evaluate Candida score >3, colonization index (CI) >0.5, corrected CI >0.4 (CCI), and Ostrosky's clinical prediction rule (CPR). Patients' characteristics and risk factors for invasive candidiasis were noted. Patients were divided into two groups; invasive candidiasis and no-invasive candidiasis.

Results:

Of 198 patients, 17 developed invasive candidiasis. Discriminatory power (area under receiver operator curve [AUROC]) for Candida score, CI, CCI, and CPR were 0.66, 0.67, 0.63, and 0.62, respectively. A large number of patients in the no-invasive candidiasis group (114 out of 181) were exposed to antifungal agents during their stay in ICU. Subgroup analysis was carried out after excluding such patients from no-invasive candidiasis group. AUROC of Candida score, CI, CCI, and CPR were 0.7, 0.7, 0.65, and 0.72, respectively, and positive predictive values (PPVs) were in the range of 25%–47%, along with negative predictive values (NPVs) in the range of 84%–96% in the subgroup analysis.

Conclusion:

Currently available risk prediction scores have good NPV but poor PPV. They are useful for selecting patients who are not likely to benefit from antifungal therapy.

Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Background

As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs.

Objective

To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence.

Methods

A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method.

Results

The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models.

Conclusions

A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

A systematic review of clinical prediction scores for deep vein thrombosis

Objective Diagnosis of deep vein thrombosis remains a challenging problem. Various clinical prediction rules have been developed in order to improve diagnosis and decision making in relation to deep vein thrombosis. The purpose of this review is to summarise the available clinical scores and describe their applicability and limitations. Methods A systematic search of PubMed, MEDLINE and EMBASE databases was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance using the keywords: clinical score, clinical prediction rule, risk assessment, clinical probability, pretest probability, diagnostic score and medical Subject Heading terms: 'Venous Thromboembolism/diagnosis' OR 'Venous Thrombosis/diagnosis'. Both development and validation studies were eligible for inclusion. Results The search strategy returned a total of 2036 articles, of which 102 articles met a priori criteria for inclusion. Eight different diagnostic scores were identified. The development of these scores differs in respect of the population included (hospital inpatients, hospital outpatients or primary care patients), the exclusion criteria, the inclusion of distal deep vein thrombosis and the use of D-dimer. The reliability and applicability of the scores in the context of specific subgroups (inpatients, cancer patients, elderly patients and those with recurrent deep vein thrombosis) remains controversial. Conclusion Detailed knowledge of the development of the various clinical prediction scores for deep vein thrombosis is essential in understanding the power, generalisability and limitations of these clinical tools.

Recovery Pathways and Prognosis After Whiplash Injury

Synopsis Recovery from a whiplash injury is varied and complex. Some individuals recover quickly and fully, while others experience ongoing pain and disability. Three distinct patterns of predicted recovery (trajectories) have been identified using disability and psychological outcome measures. These trajectories are not linear, and show that recovery, if it is going to occur, tends to happen within the first 3 months of the injury, with little improvement after this period. Identification of factors associated with poor recovery is accumulating, and since 2000 there have been at least 10 published systematic reviews on prognostic factors for whiplash-associated disorder. Poor recovery has been consistently reported to be associated with high initial neck pain intensity and neck-related disability, posttraumatic stress symptoms, pain catastrophizing, and, to a lesser extent, low self-efficacy and cold hyperalgesia. Evidence regarding factors, including compensation status, psychological factors, structural pathology, and preinjury health status, remains equivocal. Given the huge number of predictive factors and various interpretations of recovery, adapting these data for use in clinical practice is difficult. Tools such as clinical prediction rules (CPRs), by statistically quantifying relevant data, may help to predict the probability of diagnosis, prognosis, or response to treatment. Numerous CPRs have been derived for individuals with whiplash; however, to date, only 3 prognostic CPRs have undergone external validation, and none have yet undergone impact analysis, a necessary step in providing information about the rules' ability to improve clinically relevant outcomes. J Orthop Sports Phys Ther 2016;46(10):851-861. Epub 3 Sep 2016. doi:10.2519/jospt.2016.6918.

Clinical Prediction Rules That Don't Hold Up-Where to Go From Here?

Clinical prediction rules (CPRs) are created to help guide clinical decision making. To do this, they use the presence or absence of certain factors that have been shown to meaningfully predict a patient's prognosis, diagnosis, or response to treatment. While representing a seminal methodological step forward in individualized care, one of the main drawbacks of CPRs continues to be validation studies that do not support the initially derived CPR. This is particularly important because validation of CPRs in an independent patient population prior to clinical implementation is essential. Why is it quite common for existing CPRs to fall down at the validation stage? And what does this mean for research that aims to individualize treatment? J Orthop Sports Phys Ther 2016;46(7):502-505. doi:10.2519/jospt.2016.0606.

The role of CHADS2 and CHA2 DS2 -VASc scores in the prediction of stroke in individuals without atrial fibrillation: a population-based study

Essentials CHADS2 and CHA2 DS2 -VASc scores are used to predict stroke in atrial fibrillation (AF). These scores were calculated for a large cohort from the largest healthcare provider in Israel. The risk of stroke gradually increased with an increase in the scores in individuals without AF. Both scores have a relatively high performance for stroke prediction in individuals without AF. Click to hear Prof. Lowe's perspective on Arterial Thrombosis, Pathogenesis and Epidemiology

SUMMARY

Background CHADS2 and CHA2 DS2 -VASc are validated scores used to predict stroke in patients with atrial fibrillation (AF). We aimed to examine the performance of these scores in predicting stroke in individuals without AF. Methods Using the computerized database of the largest HMO in Israel, we identified all not-anticoagulated adults, aged 50 years or older on 1 January 2012. The cohort was followed for the occurrence of stroke or transient ischemic attack (TIA) until 31 December 2014. Results Of 1 053 871 individuals without AF at baseline, 34 215 developed stroke/TIA during a follow-up of 3 014 002 person-years (stroke/TIA incidence rate, 1.14 per 100 person-years). The incidence rate of stroke/TIA increased in a graded manner with increasing CHADS2 score: 0.36, 0.89, 1.89, 2.96, 4.31, 5.37 and 6.62 per 100 person-years for CHADS2 scores of 0 to 6 points, respectively (P < 0.001). Results were similar for the CHA2 DS2 -VASc score. A similar graded increasing trend in the stroke/TIA incidence rate was observed in a cohort of 46 657 patients with AF at baseline; however, stroke/TIA rates were higher in each score stratum compared with the rates of individuals without AF. The area under the receiver operating characteristic curve was 0.718 (95% CI, 0.715-0.721) and 0.714 (0.711-0.717) for CHADS2 and CHA2 DS2 -VASc scores, respectively, in individuals without AF, and 0.606 (0.598-0.614) and 0.610 (0.602-0.618), respectively, in individuals with AF. Conclusions CHADS2 and CHA2 DS2 -VASc scores have a relatively high performance for prediction of stroke/TIA in individuals without AF, which is comparable to their performance in patients with AF.

The asthma prediction rule to decrease hospitalizations for children with asthma

PURPOSE OF REVIEW

The aim of the present review was to discuss the challenges around clinical decision-making for hospitalization of children with acute asthma exacerbations and the development, internal validation, and future potential of the asthma prediction rule (APR) to provide meaningful clinical decision-support that might decrease unnecessary hospitalizations.

RECENT FINDINGS

The APR was developed and internally validated using predictor variables available before treatment in the emergency department, and performed well to predict 'need-for-hospitalization.' Oxygen saturation on room air and expiratory phase prolongation were most strongly associated with need-for-hospitalization.

SUMMARY

Research on prediction rules in pediatric asthma is rare. We developed and internally validated the APR using clinically intuitive predictor variables that are available at the bedside. Before incorporation into electronic decision-support the APR must undergo external validation and an impact analysis to determine if use of this tool will change clinician behavior and improve patient outcomes.

Impact analysis studies of clinical prediction rules relevant to primary care: a systematic review

OBJECTIVES

Following appropriate validation, clinical prediction rules (CPRs) should undergo impact analysis to evaluate their effect on patient care. The aim of this systematic review is to narratively review and critically appraise CPR impact analysis studies relevant to primary care.

SETTING

Primary care.

PARTICIPANTS

Adults and children.

INTERVENTION

Studies that implemented the CPR compared to usual care were included.

STUDY DESIGN

Randomised controlled trial (RCT), controlled before-after, and interrupted time series.

PRIMARY OUTCOME

Physician behaviour and/or patient outcomes.

RESULTS

A total of 18 studies, incorporating 14 unique CPRs, were included. The main study design was RCT (n=13). Overall, 10 studies reported an improvement in primary outcome with CPR implementation. Of 6 musculoskeletal studies, 5 were effective in altering targeted physician behaviour in ordering imaging for patients presenting with ankle, knee and neck musculoskeletal injuries. Of 6 cardiovascular studies, 4 implemented cardiovascular risk scores, and 3 reported no impact on physician behaviour outcomes, such as prescribing and referral, or patient outcomes, such as reduction in serum lipid levels. 2 studies examined CPRs in decision-making for patients presenting with chest pain and reduced inappropriate admissions. Of 5 respiratory studies, 2 were effective in reducing antibiotic prescribing for sore throat following CPR implementation. Overall, study methodological quality was often unclear due to incomplete reporting.

CONCLUSIONS

Despite increasing interest in developing and validating CPRs relevant to primary care, relatively few have gone through impact analysis. To date, research has focused on a small number of CPRs across few clinical domains only.

Coronary computed tomographic prediction rule for time-efficient guidewire crossing through chronic total occlusion: insights from the CT-RECTOR multicenter registry (Computed Tomography Registry of Chronic Total Occlusion Revascularization)

OBJECTIVES

This study sought to establish a coronary computed tomography angiography prediction rule for grading chronic total occlusion (CTO) difficulty for percutaneous coronary intervention (PCI).

BACKGROUND

The uncertainty of procedural outcome remains the strongest barrier to PCI in CTO.

METHODS

Data from 4 centers involving 240 consecutive CTO lesions with pre-procedural coronary computed tomography angiography were analyzed. Successful guidewire (GW) crossing ≤30 min was set as an endpoint to eliminate operator bias. The CT-RECTOR (Computed Tomography Registry of Chronic Total Occlusion Revascularization) score was developed by assigning 1 point for each independent predictor, and then summing all points accrued. Continuous distribution of scores was used to stratify CTO into 4 difficulty groups: easy (score 0); intermediate (score 1); difficult (score 2); and very difficult (score ≥3). Discriminatory performance was tested by 10-fold cross-validation and compared with the angiographic J-CTO (Multicenter CTO Registry of Japan) score.

RESULTS

Study endpoint was achieved in 55% of cases. Multivariable analysis yielded multiple occlusions, blunt stump, severe calcification, bending, duration of CTO ≥12 months, and previously failed PCI as independent predictors for GW crossing. The probability of successful GW crossing ≤30 min for each group (from easy to very difficult) was 95%, 88%, 57%, and 22%, respectively. Areas under receiver-operator characteristic curves for the CT-RECTOR and J-CTO scores were 0.83 and 0.71, respectively (p < 0.001). Both the original model fit and 10-fold cross-validation correctly classified 77.3% of lesions.

CONCLUSIONS

The CT-RECTOR score represents a simple and accurate noninvasive tool for predicting time-efficient GW crossing that may aid in grading CTO difficulty before PCI. (Computed Tomography Angiography Prediction Score for Percutaneous Revascularization for Chronic Total Occlusions [CT-RECTOR]; NCT02022878).

Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study

ESSENTIALS: Prediction models may help to identify patients at high risk of bleeding on antiplatelet therapy. We identified existing prediction models for bleeding and validated them in patients with cerebral ischemia. Five prediction models were identified, all of which had some methodological shortcomings. Performance in patients with cerebral ischemia was poor.

SUMMARY

Background Antiplatelet therapy is widely used in secondary prevention after a transient ischemic attack (TIA) or ischemic stroke. Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of patients at increased risk of bleeding may help target antiplatelet therapy.

OBJECTIVE

This study sought to identify existing prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy and evaluate their performance in patients with cerebral ischemia.

METHODS

We systematically searched PubMed and Embase for existing prediction models up to December 2014. The methodological quality of the included studies was assessed with the CHARMS checklist. Prediction models were externally validated in the European Stroke Prevention Study 2, comprising 6602 patients with a TIA or ischemic stroke. We assessed discrimination and calibration of included prediction models.

RESULTS

Five prediction models were identified, of which two were developed in patients with previous cerebral ischemia. Three studies assessed major bleeding, one studied intracerebral hemorrhage and one gastrointestinal bleeding. None of the studies met all criteria of good quality. External validation showed poor discriminative performance, with c-statistics ranging from 0.53 to 0.64 and poor calibration.

CONCLUSION

A limited number of prediction models is available that predict intracranial hemorrhage or major bleeding in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in patients with cerebral ischemia was poor. In order to reliably predict the risk of bleeding in patients with cerebral ischemia, development of a prediction model according to current methodological standards is needed.

The Practical Application of Clinical Prediction Rules: A Commentary Using Case Examples in Surgical Patients with Degenerative Cervical Myelopathy

Study Design Commentary. Objective This commentary aims to discuss the practical applications of a clinical prediction rule (CPR) developed to predict functional status in patients undergoing surgery for the treatment of degenerative cervical myelopathy. Methods Clinical cases from the AOSpine CSM-North America study were used to illustrate the application of a prediction rule in a surgical setting and to highlight how this CPR can be used to ultimately enhance patient care. Results A CPR combines signs and symptoms, patient characteristics, and other predictive factors to estimate disease probability, treatment prognosis, or risk of complications. These tools can influence allocation of health care resources, inform clinical decision making, and guide the design of future research studies. In a surgical setting, CPRs can be used to (1) manage patients' expectations of outcome and, in turn, improve overall satisfaction; (2) facilitate shared decision making between patient and physician; (3) identify strategies to optimize surgical results; and (4) reduce heterogeneity of care and align surgeons' perceptions of outcome with objective evidence. Conclusions Valid and clinically-relevant CPRs have tremendous value in a surgical setting.

Neutrophil to lymphocyte ratio and risk of a first episode of stroke in patients with atrial fibrillation: a cohort study

BACKGROUND

The neutrophil to lymphocyte ratio (NLR) is associated with increased risk of cardiovascular morbidity and mortality. We aimed to assess the association between NLR and first episode of stroke in patients with atrial fibrillation.

METHODS

Using the computerized database of the largest HMO in Israel, we identified a cohort of adults, aged 20 years or older, with atrial fibrillation diagnosed before 1 January 2012. Eligible subjects had no prior stroke or TIA, were not on anticoagulants at baseline, and had at least one blood cell count performed in 2011. The cohort (32,912 subjects) was followed for the first event of stroke or TIA until 31 December 2012.

RESULTS

Overall 981 subjects developed stroke during a follow-up of 30,961 person-years (stroke rate, 3.17 per 100 person-years). The incidence rate of stroke increased across NLR quartiles: 2.27, 2.72, 3.26 and 4.54 per 100 person-years, respectively. Cox proportional hazard regression analysis adjusting for the individual CHA2 DS2 -VASc score risk factors showed that, compared with the lowest NLR quartile, the HR for stroke was 1.11 (95% CI, 0.91-1.35), 1.25 (1.03-1.51) and 1.56 (1.29-1.88) for the second, third and highest quartile, respectively. On stratified analysis, NLR refined the risk of stroke across all CHA2 DS2 -VASc score strata. Adding NLR to the CHA2 DS2 -VASc score increased the AUC from 0.627 (95% CI, 0.612-0.643) to 0.635 (0.619-0.651) (P = 0.037).

CONCLUSIONS

The neutrophil to lymphocyte ratio is directly associated with the risk of stroke in patients with atrial fibrillation. Future studies are needed to replicate these findings.

CURB-65 and other markers of illness severity in community-acquired pneumonia among HIV-positive patients

As the relative burden of community-acquired bacterial pneumonia among HIV-positive patients increases, adequate prediction of case severity on presentation is crucial. We sought to determine what characteristics measurable on presentation are predictive of worse outcomes. We studied all admissions for community-acquired bacterial pneumonia over one year at a tertiary centre. Patient demographics, comorbidities, HIV-specific markers and CURB-65 scores on Emergency Department presentation were reviewed. Outcomes of interest included mortality, bacteraemia, intensive care unit admission and orotracheal intubation. A total of 396 patients were included: 49 HIV-positive and 347 HIV-negative. Mean CURB-65 score was 1.3 for HIV-positive and 2.2 for HIV-negative patients (p < 0.0001), its predictive value for mortality being maintained in both groups (p = 0.03 and p < 0.001, respectively). Adjusting for CURB-65 scores, HIV infection by itself was only associated with bacteraemia (adjusted odds ratio [AOR] 7.1, 95% CI [2.6-19.5]). Patients with < 200 CD4 cells/µL presented similar CURB-65 adjusted mortality (aOR 1.7, 95% CI [0.2-15.2]), but higher risk of intensive care unit admission (aOR 5.7, 95% CI [1.5-22.0]) and orotracheal intubation (aOR 9.1, 95% CI [2.2-37.1]), compared to HIV-negative patients. These two associations were not observed in the > 200 CD4 cells/µL subgroup (aOR 2.2, 95% CI [0.7-7.6] and aOR 0.8, 95% CI [0.1-6.5], respectively). Antiretroviral therapy and viral load suppression were not associated with different outcomes (p > 0.05). High CURB-65 scores and CD4 counts < 200 cells/µL were both associated with worse outcomes. Severity assessment scales and CD4 counts may both be helpful in predicting severity in HIV-positive patients presenting with community-acquired bacterial pneumonia.

Baseline Examination Factors Associated With Clinical Improvement After Dry Needling in Individuals With Low Back Pain

STUDY DESIGN

Quasi-experimental.

OBJECTIVES

To explore for associations between demographic, patient history, and physical examination variables and short-term improvement in self-reported disability following dry needling therapy performed on individuals with low back pain (LBP).

BACKGROUND

Dry needling is an intervention used with increasing frequency in patients with LBP; however, the characteristics of patients who are most likely to respond are not known.

METHODS

Seventy-two volunteers with mechanical LBP participated in the study. Potential prognostic factors were collected from baseline questionnaires, patient history, and physical examination tests. Treatment consisted of dry needling to the lumbar multifidus muscles bilaterally, administered during a single treatment session. Improvement was based on percent change on the Oswestry Disability Index at 1 week. The univariate and multivariate associations between 33 potential prognostic factors and improved disability were assessed with correlation coefficients and multivariate linear regression.

RESULTS

Increased LBP with the multifidus lift test (rpb = 0.31, P = .01) or during passive hip flexion performed with the patient supine (rpb = 0.23, P = .06), as well as positive beliefs about acupuncture/dry needling (rho = 0.22, P = .07), demonstrated univariate associations with Oswestry Disability Index improvement. Aggravation of LBP with standing (rpb = -0.27, P = .03), presence of leg pain (rpb = -0.29, P = .02), and any perception of hypermobility in the lumbar spine (rpb = -0.21, P = .09) were associated with less improvement. The multivariate model identified 2 predictors of improved disability with dry needling: pain with the multifidus lift test and no aggravation with standing (R(2) = 0.16, P = .01).

CONCLUSION

Increased LBP with the multifidus lift test was the strongest predictor of improved disability after dry needling, suggesting that the finding of pain during muscle contraction should be studied in future dry needling studies.

LEVEL OF EVIDENCE

Prognosis, level 1b.

A clinical prediction rule for meniscal tears in primary care: development and internal validation using a multicentre study

BACKGROUND

In primary care, meniscal tears are difficult to detect. A quick and easy clinical prediction rule based on patient history and a single meniscal test may help physicians to identify high-risk patients for referral for magnetic resonance imaging (MRI).

AIM

The study objective was to develop and internally validate a clinical prediction rule (CPR) for the detection of meniscal tears in primary care.

DESIGN AND SETTING

In a cross-sectional multicentre study, 121 participants from primary care were included if they were aged 18-65 years with knee complaints that existed for <6 months, and who were suspected to suffer from a meniscal tear.

METHOD

One diagnostic physical meniscal test and 14 clinical variables were considered to be predictors of MRI outcome. Using known predictors for the presence of meniscal tears, a 'quick and easy' CPR was derived.

RESULTS

The final CPR included the variables sex, age, weight-bearing during trauma, performing sports, effusion, warmth, discolouration, and Deep Squat test. The final model had an AUC of 0.76 (95% CI = 0.72 to 0.80). A cut-point of 150 points yielded an overall sensitivity of 86.1% and a specificity of 45.5%. For this cut-point, the positive predictive value was 55.0%, and the negative predictive value was 81.1%. A scoring system was provided including the corresponding predicted probabilities for a meniscal tear.

CONCLUSION

The CPR improved the detection of meniscal tears in primary care. Further evaluation of the CPR in new primary care patients is needed, however, to assess its usefulness.