Browsing by Subject "Predictive Value of Tests"

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    ItemOpen Access
    A blood-based biomarker panel to risk-stratify mild traumatic brain injury.
    (PloS one, 2017-01) Sharma, Richa; Rosenberg, Alexandra; Bennett, Ellen R; Laskowitz, Daniel T; Acheson, Shawn K
    Mild traumatic brain injury (TBI) accounts for the vast majority of the nearly two million brain injuries suffered in the United States each year. Mild TBI is commonly classified as complicated (radiographic evidence of intracranial injury) or uncomplicated (radiographically negative). Such a distinction is important because it helps to determine the need for further neuroimaging, potential admission, or neurosurgical intervention. Unfortunately, imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are costly and not without some risk. The purpose of this study was to screen 87 serum biomarkers to identify a select panel of biomarkers that would predict the presence of intracranial injury as determined by initial brain CT. Serum was collected from 110 patients who sustained a mild TBI within 24 hours of blood draw. Two models were created. In the broad inclusive model, 72kDa type IV collagenase (MMP-2), C-reactive protein (CRP), creatine kinase B type (CKBB), fatty acid binding protein-heart (hFABP), granulocyte-macrophage colony-stimulating factor (GM-CSF) and malondialdehyde modified low density lipoprotein (MDA-LDL) significantly predicted injury visualized on CT, yielding an overall c-statistic of 0.975 and a negative predictive value (NPV) of 98.6. In the parsimonious model, MMP-2, CRP, and CKBB type significantly predicted injury visualized on CT, yielding an overall c-statistic of 0.964 and a negative predictive value (NPV) of 97.2. These results suggest that a serum based biomarker panel can accurately differentiate patients with complicated mild TBI from those with uncomplicated mild TBI. Such a panel could be useful to guide early triage decisions, including the need for further evaluation or admission, especially in those environments in which resources are limited.
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    A clinical prediction model for long-term functional outcome after traumatic spinal cord injury based on acute clinical and imaging factors.
    (Journal of neurotrauma, 2012-09) Wilson, Jefferson R; Grossman, Robert G; Frankowski, Ralph F; Kiss, Alexander; Davis, Aileen M; Kulkarni, Abhaya V; Harrop, James S; Aarabi, Bizhan; Vaccaro, Alexander; Tator, Charles H; Dvorak, Marcel; Shaffrey, Christopher I; Harkema, Susan; Guest, James D; Fehlings, Michael G
    To improve clinicians' ability to predict outcome after spinal cord injury (SCI) and to help classify patients within clinical trials, we have created a novel prediction model relating acute clinical and imaging information to functional outcome at 1 year. Data were obtained from two large prospective SCI datasets. Functional independence measure (FIM) motor score at 1 year follow-up was the primary outcome, and functional independence (score ≥ 6 for each FIM motor item) was the secondary outcome. A linear regression model was created with the primary outcome modeled relative to clinical and imaging predictors obtained within 3 days of injury. A logistic model was then created using the dichotomized secondary outcome and the same predictor variables. Model validation was performed using a bootstrap resampling procedure. Of 729 patients, 376 met the inclusion criteria. The mean FIM motor score at 1 year was 62.9 (±28.6). Better functional status was predicted by less severe initial American Spinal Injury Association (ASIA) Impairment Scale grade, and by an ASIA motor score >50 at admission. In contrast, older age and magnetic resonance imaging (MRI) signal characteristics consistent with spinal cord edema or hemorrhage predicted worse functional outcome. The linear model predicting FIM motor score demonstrated an R-square of 0.52 in the original dataset, and 0.52 (95% CI 0.52,0.53) across the 200 bootstraps. Functional independence was achieved by 148 patients (39.4%). For the logistic model, the area under the curve was 0.93 in the original dataset, and 0.92 (95% CI 0.92,0.93) across the bootstraps, indicating excellent predictive discrimination. These models will have important clinical impact to guide decision making and to counsel patients and families.
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    A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.
    (J Alzheimers Dis, 2014) Razlighi, Qolamreza R; Stallard, Eric; Brandt, Jason; Blacker, Deborah; Albert, Marilyn; Scarmeas, Nikolaos; Kinosian, Bruce; Yashin, Anatoliy I; Stern, Yaakov
    BACKGROUND: The ability to predict the length of time to death and institutionalization has strong implications for Alzheimer's disease patients and caregivers, health policy, economics, and the design of intervention studies. OBJECTIVE: To develop and validate a prediction algorithm that uses data from a single visit to estimate time to important disease endpoints for individual Alzheimer's disease patients. METHOD: Two separate study cohorts (Predictors 1, N = 252; Predictors 2, N = 254), all initially with mild Alzheimer's disease, were followed for 10 years at three research centers with semiannual assessments that included cognition, functional capacity, and medical, psychiatric, and neurologic information. The prediction algorithm was based on a longitudinal Grade of Membership model developed using the complete series of semiannually-collected Predictors 1 data. The algorithm was validated on the Predictors 2 data using data only from the initial assessment to predict separate survival curves for three outcomes. RESULTS: For each of the three outcome measures, the predicted survival curves fell well within the 95% confidence intervals of the observed survival curves. Patients were also divided into quintiles for each endpoint to assess the calibration of the algorithm for extreme patient profiles. In all cases, the actual and predicted survival curves were statistically equivalent. Predictive accuracy was maintained even when key baseline variables were excluded, demonstrating the high resilience of the algorithm to missing data. CONCLUSION: The new prediction algorithm accurately predicts time to death, institutionalization, and need for full-time care in individual Alzheimer's disease patients; it can be readily adapted to predict other important disease endpoints. The algorithm will serve an unmet clinical, research, and public health need.
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    A new predictive model for an improved respiratory isolation strategy in HIV-infected patients with PTB.
    (The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease, 2014-07) Carugati, M; Schiroli, C; Zanini, F; Vanoni, N; Galli, M; Adorni, F; Franzetti, F

    Setting

    Luigi Sacco Hospital, Milan, Italy, 1 January 2000-31 December 2010.

    Objectives

    To develop a predictive score for identifying human immunodeficiency virus (HIV) infected patients with pulmonary tuberculosis (PTB).

    Design

    Retrospective study based on the medical charts of HIV-infected patients admitted consecutively on presumption of PTB. Patients with culture-positive TB were included in the TB group. Culture-negative subjects formed the non-TB group. Risk factors for PTB were identified and a predictive model was developed. The diagnostic test accuracy of the derived score and that of previously developed scores were analysed.

    Results

    A total of 65 patients were included in the TB group and 505 subjects in the non-TB group. An eight-variable model (age, origin, alcohol use, respiratory rate, weight loss, haemoglobin, white blood cell count, typical chest X-ray) was derived. When compared with the different scores, this model showed the greatest area under the receiver operating characteristic curve (0.880). This score was the only one to present a negative likelihood ratio of <0.2, which is the threshold for giving strong diagnostic evidence against TB.

    Conclusions

    This model may be useful in predicting PTB in HIV patients in low-endemic countries. A validation study is necessary.
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    A predictive model and nomogram for predicting return to work at 3 months after cervical spine surgery: an analysis from the Quality Outcomes Database.
    (Neurosurgical focus, 2018-11) Devin, Clinton J; Bydon, Mohamad; Alvi, Mohammed Ali; Kerezoudis, Panagiotis; Khan, Inamullah; Sivaganesan, Ahilan; McGirt, Matthew J; Archer, Kristin R; Foley, Kevin T; Mummaneni, Praveen V; Bisson, Erica F; Knightly, John J; Shaffrey, Christopher I; Asher, Anthony L
    OBJECTIVEBack pain and neck pain are two of the most common causes of work loss due to disability, which poses an economic burden on society. Due to recent changes in healthcare policies, patient-centered outcomes including return to work have been increasingly prioritized by physicians and hospitals to optimize healthcare delivery. In this study, the authors used a national spine registry to identify clinical factors associated with return to work at 3 months among patients undergoing a cervical spine surgery.METHODSThe authors queried the Quality Outcomes Database registry for information collected from April 2013 through March 2017 for preoperatively employed patients undergoing cervical spine surgery for degenerative spine disease. Covariates included demographic, clinical, and operative variables, and baseline patient-reported outcomes. Multiple imputations were used for missing values and multivariable logistic regression analysis was used to identify factors associated with higher odds of returning to work. Bootstrap resampling (200 iterations) was used to assess the validity of the model. A nomogram was constructed using the results of the multivariable model.RESULTSA total of 4689 patients were analyzed, of whom 82.2% (n = 3854) returned to work at 3 months postoperatively. Among previously employed and working patients, 89.3% (n = 3443) returned to work compared to 52.3% (n = 411) among those who were employed but not working (e.g., were on a leave) at the time of surgery (p < 0.001). On multivariable logistic regression the authors found that patients who were less likely to return to work were older (age > 56-65 years: OR 0.69, 95% CI 0.57-0.85, p < 0.001; age > 65 years: OR 0.65, 95% CI 0.43-0.97, p = 0.02); were employed but not working (OR 0.24, 95% CI 0.20-0.29, p < 0.001); were employed part time (OR 0.56, 95% CI 0.42-0.76, p < 0.001); had a heavy-intensity (OR 0.42, 95% CI 0.32-0.54, p < 0.001) or medium-intensity (OR 0.59, 95% CI 0.46-0.76, p < 0.001) occupation compared to a sedentary occupation type; had workers' compensation (OR 0.38, 95% CI 0.28-0.53, p < 0.001); had a higher Neck Disability Index score at baseline (OR 0.60, 95% CI 0.51-0.70, p = 0.017); were more likely to present with myelopathy (OR 0.52, 95% CI 0.42-0.63, p < 0.001); and had more levels fused (3-5 levels: OR 0.46, 95% CI 0.35-0.61, p < 0.001). Using the multivariable analysis, the authors then constructed a nomogram to predict return to work, which was found to have an area under the curve of 0.812 and good validity.CONCLUSIONSReturn to work is a crucial outcome that is being increasingly prioritized for employed patients undergoing spine surgery. The results from this study could help surgeons identify at-risk patients so that preoperative expectations could be discussed more comprehensively.
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    A risk score for in-hospital death in patients admitted with ischemic or hemorrhagic stroke.
    (J Am Heart Assoc, 2013-01-28) Smith, Eric E; Shobha, Nandavar; Dai, David; Olson, DaiWai M; Reeves, Mathew J; Saver, Jeffrey L; Hernandez, Adrian F; Peterson, Eric D; Fonarow, Gregg C; Schwamm, Lee H
    BACKGROUND: We aimed to derive and validate a single risk score for predicting death from ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). METHODS AND RESULTS: Data from 333 865 stroke patients (IS, 82.4%; ICH, 11.2%; SAH, 2.6%; uncertain type, 3.8%) in the Get With The Guidelines-Stroke database were used. In-hospital mortality varied greatly according to stroke type (IS, 5.5%; ICH, 27.2%; SAH, 25.1%; unknown type, 6.0%; P<0.001). The patients were randomly divided into derivation (60%) and validation (40%) samples. Logistic regression was used to determine the independent predictors of mortality and to assign point scores for a prediction model in the overall population and in the subset with the National Institutes of Health Stroke Scale (NIHSS) recorded (37.1%). The c statistic, a measure of how well the models discriminate the risk of death, was 0.78 in the overall validation sample and 0.86 in the model including NIHSS. The model with NIHSS performed nearly as well in each stroke type as in the overall model including all types (c statistics for IS alone, 0.85; for ICH alone, 0.83; for SAH alone, 0.83; uncertain type alone, 0.86). The calibration of the model was excellent, as demonstrated by plots of observed versus predicted mortality. CONCLUSIONS: A single prediction score for all stroke types can be used to predict risk of in-hospital death following stroke admission. Incorporation of NIHSS information substantially improves this predictive accuracy.
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    Adult age differences in frontostriatal representation of prediction error but not reward outcome.
    (Cogn Affect Behav Neurosci, 2014-06) Samanez-Larkin, Gregory R; Worthy, Darrell A; Mata, Rui; McClure, Samuel M; Knutson, Brian
    Emerging evidence from decision neuroscience suggests that although younger and older adults show similar frontostriatal representations of reward magnitude, older adults often show deficits in feedback-driven reinforcement learning. In the present study, healthy adults completed reward-based tasks that did or did not depend on probabilistic learning, while undergoing functional neuroimaging. We observed reductions in the frontostriatal representation of prediction errors during probabilistic learning in older adults. In contrast, we found evidence for stability across adulthood in the representation of reward outcome in a task that did not require learning. Together, the results identify changes across adulthood in the dynamic coding of relational representations of feedback, in spite of preserved reward sensitivity in old age. Overall, the results suggest that the neural representation of prediction error, but not reward outcome, is reduced in old age. These findings reveal a potential dissociation between cognition and motivation with age and identify a potential mechanism for explaining changes in learning-dependent decision making in old adulthood.
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    An evaluation of remifentanil-sevoflurane response surface models in patients emerging from anesthesia: model improvement using effect-site sevoflurane concentrations.
    (Anesth Analg, 2010-08) Johnson, Ken B; Syroid, Noah D; Gupta, Dhanesh K; Manyam, Sandeep C; Pace, Nathan L; LaPierre, Cris D; Egan, Talmage D; White, Julia L; Tyler, Diane; Westenskow, Dwayne R
    INTRODUCTION: We previously reported models that characterized the synergistic interaction between remifentanil and sevoflurane in blunting responses to verbal and painful stimuli. This preliminary study evaluated the ability of these models to predict a return of responsiveness during emergence from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non-steady-state conditions, accounting for the lag time between sevoflurane effect-site concentration (Ce) and end-tidal (ET) concentration would improve predictions. METHODS: Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET-based and a Ce-based prediction. Similarly, 2 predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared with observations with graphical and temporal analyses. RESULTS: While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (> or = 99%). However, after termination of the anesthetic, models exhibited a wide range of predictions at emergence (1%-97%). Although wide, the Ce-based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET-based predictions. For the ET-based model, 45% of the patients awoke within 2 min of the 50% model predicted probability of unresponsiveness and 65% awoke within 4 min. For the Ce-based model, 45% of the patients awoke within 1 min of the 50% model predicted probability of unresponsiveness and 85% awoke within 3.2 min. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce- and ET-based models. DISCUSSION: Results confirmed, in part, our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large-scale evaluations with numerous patients are needed to better characterize model performance.
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    ItemOpen Access
    Application of the estrogen threshold hypothesis to the physiologic hypoestrogenemia of lactation.
    (Breastfeeding medicine : the official journal of the Academy of Breastfeeding Medicine, 2015-03) Agarwal, Sanjay K; Kim, Julie; Korst, Lisa M; Hughes, Claude L
    OBJECTIVE: This study determined the impact of breastfeeding on hypoestrogenic symptoms among women in the postpartum period and correlated these findings with the Estrogen Threshold Hypothesis, which postulates that the hypoestrogenic symptoms experienced are related to circulating estrogen levels. STUDY DESIGN: Using a survey instrument that combined previously validated assessments of postpartum mood changes and menopausal symptoms, women were evaluated in the immediate postpartum period, prior to hospital discharge, and at 3 and 6 weeks postpartum. Each time period was analyzed independently, in a cross-sectional design, where women were categorized as "breastfeeding" or "bottle feeding." RESULTS: Of 236 women recruited, 171 (72.5%) intended to breastfeed, and 62 (26.3%) intended to bottle feed. At both the 3- and 6-week postpartum evaluations, a similar percentage of women in the breastfeeding and bottle-feeding groups reported hot flashes. However, breastfeeding women were more likely to report vaginal dryness than those who did not breastfeed: 20/150 (13.3%) versus 3/80 (3.8%) at 3 weeks, p<0.05; 25/143 (17.5%) versus 2/87 (2.3%) at 6 weeks, p<0.001. CONCLUSIONS: The Estrogen Threshold Hypothesis accurately predicts the findings of increased reported vaginal dryness but not hot flashes during lactation.
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    Are Higher Global Alignment and Proportion Scores Associated With Increased Risks of Mechanical Complications After Adult Spinal Deformity Surgery? An External Validation.
    (Clinical orthopaedics and related research, 2021-02) Kwan, Kenny Yat Hong; Lenke, Lawrence G; Shaffrey, Christopher I; Carreon, Leah Y; Dahl, Benny T; Fehlings, Michael G; Ames, Christopher P; Boachie-Adjei, Oheneba; Dekutoski, Mark B; Kebaish, Khaled M; Lewis, Stephen J; Matsuyama, Yukihiro; Mehdian, Hossein; Qiu, Yong; Schwab, Frank J; Cheung, Kenneth Man Chee; AO Spine Knowledge Forum Deformity

    Background

    The Global Alignment and Proportion (GAP) score, based on pelvic incidence-based proportional parameters, was recently developed to predict mechanical complications after surgery for spinal deformities in adults. However, this score has not been validated in an independent external dataset.

    Questions/purposes

    After adult spinal deformity surgery, is a higher GAP score associated with (1) an increased risk of mechanical complications, defined as rod fractures, implant-related complications, proximal or distal junctional kyphosis or failure; (2) a higher likelihood of undergoing revision surgery to treat a mechanical complication; and (3) is a lower (more proportioned) GAP score category associated with better validated outcomes scores using the Oswestry Disability Index (ODI), Scoliosis Research Society-22 (SRS-22) and the Short Form-36 questionnaires?

    Methods

    A total of 272 patients who had undergone corrective surgeries for complex spinal deformities were enrolled in the Scoli-RISK-1 prospective trial. Patients were included in this secondary analysis if they fulfilled the original inclusion criteria by Yilgor et al. From the original 272 patients, 14% (39) did not satisfy the radiographic inclusion criteria, the GAP score could not be calculated in 14% (37), and 24% (64) did not have radiographic assessment at postoperative 2 years, leaving 59% (159) for analysis in this review of data from the original trial. A total of 159 patients were included in this study,with a mean age of 58 ± 14 years at the time of surgery. Most patients were female (72%, 115 of 159), the mean number of levels involved in surgery was 12 ± 4, and three-column osteotomy was performed in 76% (120 of 159) of patients. The GAP score was calculated using parameters from early postoperative radiographs (between 3 and 12 weeks) including pelvic incidence, sacral slope, lumbar lordosis, lower arc lordosis and global tilt, which were independently obtained from a computer software based on centralized patient radiographs. The GAP score was categorized as proportional (scores of 0 to 2), moderately disproportional (scores of 3 to 6), or severely disproportional (scores higher than 7 to 13). Receiver operating characteristic area under curve (AUC) was used to assess associations between GAP score and risk of mechanical complications and risk of revision surgery. An AUC of 0.5 to 0.7 was classified as "no or low associative power", 0.7 to 0.9 as "moderate" and greater than 0.9 as "high". We analyzed differences in validated outcome scores between the GAP categories using Wilcoxon rank sum test.

    Results

    At a minimum of 2 years' follow-up, a higher GAP score was not associated with increased risks of mechanical complications (AUC = 0.60 [95% CI 0.50 to 0.70]). A higher GAP score was not associated with a higher likelihood of undergoing a revision surgery to treat a mechanical complication (AUC = 0.66 [95% 0.53 to 0.78]). However, a moderately disproportioned GAP score category was associated with better SF-36 physical component summary score (36 ± 10 versus 40 ± 11; p = 0.047), better SF-36 mental component summary score (46 ± 13 versus 51 ± 12; p = 0.01), better SRS-22 total score (3.4 ± 0.8 versus 3.7 ± 0.7, p = 0.02) and better ODI score (35 ± 21 versus 25 ± 20; p = 0.003) than severely disproportioned GAP score category.

    Conclusion

    Based on the findings of this external validation study, we found that alignment targets based on the GAP score alone were not associated with increased risks of mechanical complications and mechanical revisions in patients with complex adult spinal disorders. Parameters not included in the original GAP score needed to be considered to reduce the likelihood of mechanical complications.

    Level of evidence

    Level III, diagnostic study.
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    Association of a Network of Immunologic Response and Clinical Features With the Functional Recovery From Crotalinae Snakebite Envenoming.
    (Frontiers in immunology, 2021-01) Gerardo, Charles J; Silvius, Elizabeth; Schobel, Seth; Eppensteiner, John C; McGowan, Lauren M; Elster, Eric A; Kirk, Allan D; Limkakeng, Alexander T

    Background

    The immunologic pathways activated during snakebite envenoming (SBE) are poorly described, and their association with recovery is unclear. The immunologic response in SBE could inform a prognostic model to predict recovery. The purpose of this study was to develop pre- and post-antivenom prognostic models comprised of clinical features and immunologic cytokine data that are associated with recovery from SBE.

    Materials and methods

    We performed a prospective cohort study in an academic medical center emergency department. We enrolled consecutive patients with Crotalinae SBE and obtained serum samples based on previously described criteria for the Surgical Critical Care Initiative (SC2i)(ClinicalTrials.gov Identifier: NCT02182180). We assessed a standard set of clinical variables and measured 35 unique cytokines using Luminex Cytokine 35-Plex Human Panel pre- and post-antivenom administration. The Patient-Specific Functional Scale (PSFS), a well-validated patient-reported outcome of functional recovery, was assessed at 0, 7, 14, 21 and 28 days and the area under the patient curve (PSFS AUPC) determined. We performed Bayesian Belief Network (BBN) modeling to represent relationships with a diagram composed of nodes and arcs. Each node represents a cytokine or clinical feature and each arc represents a joint-probability distribution (JPD).

    Results

    Twenty-eight SBE patients were enrolled. Preliminary results from 24 patients with clinical data, 9 patients with pre-antivenom and 11 patients with post-antivenom cytokine data are presented. The group was mostly female (82%) with a mean age of 38.1 (SD ± 9.8) years. In the pre-antivenom model, the variables most closely associated with the PSFS AUPC are predominantly clinical features. In the post-antivenom model, cytokines are more fully incorporated into the model. The variables most closely associated with the PSFS AUPC are age, antihistamines, white blood cell count (WBC), HGF, CCL5 and VEGF. The most influential variables are age, antihistamines and EGF. Both the pre- and post-antivenom models perform well with AUCs of 0.87 and 0.90 respectively.

    Discussion

    Pre- and post-antivenom networks of cytokines and clinical features were associated with functional recovery measured by the PSFS AUPC over 28 days. With additional data, we can identify prognostic models using immunologic and clinical variables to predict recovery from SBE.
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    Automatic identification of variables in epidemiological datasets using logic regression.
    (BMC medical informatics and decision making, 2017-04) Lorenz, Matthias W; Abdi, Negin Ashtiani; Scheckenbach, Frank; Pflug, Anja; Bülbül, Alpaslan; Catapano, Alberico L; Agewall, Stefan; Ezhov, Marat; Bots, Michiel L; Kiechl, Stefan; Orth, Andreas; PROG-IMT study group

    Background

    For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable.

    Methods

    For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated.

    Results

    In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables.

    Conclusions

    We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.
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    Bone scan positivity in non-metastatic, castrate-resistant prostate cancer: external validation study.
    (International braz j urol : official journal of the Brazilian Society of Urology, 2020-01) Johnston, Ashley W; Longo, Thomas A; Davis, Leah Gerber; Zapata, Daniel; Freedland, Stephen J; Routh, Jonathan C

    Introduction

    Tables predicting the probability of a positive bone scan in men with non-metastatic, castrate-resistant prostate cancer have recently been reported. We performed an external validation study of these bone scan positivity tables.

    Materials and methods

    We performed a retrospective cohort study of patients seen at a tertiary care medical center (1996-2012) to select patients with non-metastatic, castrate-resistant prostate cancer. Abstracted data included demographic, anthropometric, and disease-specific data such as patient race, BMI, PSA kinetics, and primary treatment. Primary outcome was metastasis on bone scan. Multivariable logistic regression was performed using generalized estimating equations to adjust for repeated measures. Risk table performance was assessed using ROC curves.

    Results

    We identified 6.509 patients with prostate cancer who had received hormonal therapy with a post-hormonal therapy PSA ≥2ng/mL, 363 of whom had non-metastatic, castrate-resistant prostate cancer. Of these, 187 patients (356 bone scans) had calculable PSA kinetics and ≥1 bone scan. Median follow-up after castrate-resistant prostate cancer diagnosis was 32 months (IQR: 19-48). There were 227 (64%) negative and 129 (36%) positive bone scans. On multivariable analysis, higher PSA at castrate-resistant prostate cancer (4.67 vs. 4.4ng/mL, OR=0.57, P=0.02), shorter time from castrate-resistant prostate cancer to scan (7.9 vs. 14.6 months, OR=0.97, P=0.006) and higher PSA at scan (OR=2.91, P<0.0001) were significantly predictive of bone scan positivity. The AUC of the previously published risk tables for predicting scan positivity was 0.72.

    Conclusion

    Previously published risk tables predicted bone scan positivity in men with non-metastatic, castrate-resistant prostate cancer with reasonable accuracy.
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    Can psychological features predict antidepressant response to rTMS? A Discovery-Replication approach.
    (Psychological medicine, 2020-01) Krepel, Noralie; Rush, A John; Iseger, Tabitha A; Sack, Alexander T; Arns, Martijn

    Background

    Few studies focused on the relationship between psychological measures, major depressive disorder (MDD) and repetitive transcranial magnetic stimulation (rTMS) response. This study investigated several psychological measures as potential predictors for rTMS treatment response. Additionally, this study employed two approaches to evaluate the robustness of our findings by implementing immediate replication and full-sample exploration with strict p-thresholding.

    Methods

    This study is an open-label, multi-site study with a total of 196 MDD patients. The sample was subdivided in a Discovery (60% of total sample, n = 119) and Replication sample (40% of total sample, n = 77). Patients were treated with right low frequency (1 Hz) or left high frequency (10 Hz) rTMS at the dorsolateral prefrontal cortex. Clinical variables [Beck Depression Inventory (BDI), Neuroticism, Extraversion, Openness Five-Factor Inventory, and Depression, Anxiety, and Stress Scale, and BDI subscales] were obtained at baseline, post-treatment, and at follow-up. Predictors were analyzed in terms of statistical association, robustness (independent replication), as well as for their clinical relevance [positive predictive value (PPV) and negative predictive value (NPV)].

    Results

    Univariate analyses revealed that non-responders had higher baseline anhedonia scores. Anhedonia scores at baseline correlated negatively with total BDI percentage change over time. This finding was replicated. However, anhedonia scores showed to be marginally predictive of rTMS response, and neither PPV nor NPV reached the levels of clinical relevance.

    Conclusions

    This study suggests that non-responders to rTMS treatment have higher baseline anhedonia scores. However, anhedonia was only marginally predictive of rTMS response. Since all other psychological measures did not show predictive value, it is concluded that psychological measures cannot be used as clinically relevant predictors to rTMS response in MDD.
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    Change in classification grade by the SRS-Schwab Adult Spinal Deformity Classification predicts impact on health-related quality of life measures: prospective analysis of operative and nonoperative treatment.
    (Spine, 2013-09) Smith, Justin S; Klineberg, Eric; Schwab, Frank; Shaffrey, Christopher I; Moal, Bertrand; Ames, Christopher P; Hostin, Richard; Fu, Kai-Ming G; Burton, Douglas; Akbarnia, Behrooz; Gupta, Munish; Hart, Robert; Bess, Shay; Lafage, Virginie; International Spine Study Group

    Study design

    Multicenter, prospective, consecutive series.

    Objective

    To evaluate responsiveness of the Scoliosis Research Society (SRS)-Schwab adult spinal deformity (ASD) classification to changes in health-related quality of life (HRQOL) after treatment for ASD.

    Summary of background data

    Ideally, a classification system should describe and be responsive to changes in a disease state. We hypothesized that the SRS-Schwab classification is responsive to changes in HRQOL measures after treatment for ASD.

    Methods

    A multicenter, prospective, consecutive series from the International Spine Study Group.

    Inclusion criteria

    ASD, age more than 18, operative or nonoperative treatment, baseline and 1-year radiographs, and HRQOL measures (Oswestry Disability Index [ODI], SRS-22, Short Form [SF]-36). The SRS-Schwab classification includes a curve descriptor and 3 sagittal spinopelvic modifiers (sagittal vertical axis [SVA], pelvic tilt, pelvic incidence/lumbar lordosis [PI-LL] mismatch). Changes in modifiers at 1 year were assessed for impact on HRQOL from pretreatment values based on minimal clinically important differences.

    Results

    Three hundred forty-one patients met criteria (mean age = 54; 85% females; 177 operative and 164 nonoperative). Change in pelvic tilt modifier at 1-year follow-up was associated with changes in ODI and SRS-22 (total and appearance scores) (P ≤ 0.034). Change in SVA modifier at 1 year was associated with changes in ODI, SF-36 physical component score, and SRS-22 (total, activity, and appearance scores) (P ≤ 0.037). Change in PI-LL modifier at 1 year was associated with changes in SF-36 physical component score and SRS-22 (total, activity, and appearance scores) (P ≤ 0.03). Patients with improvement of pelvic tilt, SVA, or PI-LL modifiers were significantly more likely to achieve minimal clinically important difference for ODI, SF-36 physical component score (SVA and PI-LL only), SRS activity, and SRS pain (PI-LL only).

    Conclusion

    The SRS-Schwab classification provides a validated system to evaluate ASD, and the classification components correlate with HRQOL measures. This study demonstrates that the classification modifiers are responsive to changes in disease state and reflect significant changes in patient-reported outcomes.

    Level of evidence

    3.
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    Chapter 11: challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators.
    (Journal of general internal medicine, 2012-06) Jonas, Daniel E; Wilt, Timothy J; Taylor, Brent C; Wilkins, Tania M; Matchar, David B
    In this paper, we discuss common challenges in and principles for conducting systematic reviews of genetic tests. The types of genetic tests discussed are those used to 1). determine risk or susceptibility in asymptomatic individuals; 2). reveal prognostic information to guide clinical management in those with a condition; or 3). predict response to treatments or environmental factors. This paper is not intended to provide comprehensive guidance on evaluating all genetic tests. Rather, it focuses on issues that have been of particular concern to analysts and stakeholders and on areas that are of particular relevance for the evaluation of studies of genetic tests. The key points include: The general principles that apply in evaluating genetic tests are similar to those for other prognostic or predictive tests, but there are differences in how the principles need to be applied or the degree to which certain issues are relevant. A clear definition of the clinical scenario and an analytic framework is important when evaluating any test, including genetic tests. Organizing frameworks and analytic frameworks are useful constructs for approaching the evaluation of genetic tests. In constructing an analytic framework for evaluating a genetic test, analysts should consider preanalytic, analytic, and postanalytic factors; such factors are useful when assessing analytic validity. Predictive genetic tests are generally characterized by a delayed time between testing and clinically important events. Finding published information on the analytic validity of some genetic tests may be difficult. Web sites (FDA or diagnostic companies) and gray literature may be important sources. In situations where clinical factors associated with risk are well characterized, comparative effectiveness reviews should assess the added value of using genetic testing along with known factors compared with using the known factors alone. For genome-wide association studies, reviewers should determine whether the association has been validated in multiple studies to minimize both potential confounding and publication bias. In addition, reviewers should note whether appropriate adjustments for multiple comparisons were used.
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    Clinician judgment vs formal scales for predicting intracerebral hemorrhage outcomes.
    (Neurology, 2016-01-12) Hwang, David Y; Dell, Cameron A; Sparks, Mary J; Watson, Tiffany D; Langefeld, Carl D; Comeau, Mary E; Rosand, Jonathan; Battey, Thomas WK; Koch, Sebastian; Perez, Mario L; James, Michael L; McFarlin, Jessica; Osborne, Jennifer L; Woo, Daniel; Kittner, Steven J; Sheth, Kevin N
    OBJECTIVE: To compare the performance of formal prognostic instruments vs subjective clinical judgment with regards to predicting functional outcome in patients with spontaneous intracerebral hemorrhage (ICH). METHODS: This prospective observational study enrolled 121 ICH patients hospitalized at 5 US tertiary care centers. Within 24 hours of each patient's admission to the hospital, one physician and one nurse on each patient's clinical team were each asked to predict the patient's modified Rankin Scale (mRS) score at 3 months and to indicate whether he or she would recommend comfort measures. The admission ICH score and FUNC score, 2 prognostic scales selected for their common use in neurologic practice, were calculated for each patient. Spearman rank correlation coefficients (r) with respect to patients' actual 3-month mRS for the physician and nursing predictions were compared against the same correlation coefficients for the ICH score and FUNC score. RESULTS: The absolute value of the correlation coefficient for physician predictions with respect to actual outcome (0.75) was higher than that of either the ICH score (0.62, p = 0.057) or the FUNC score (0.56, p = 0.01). The nursing predictions of outcome (r = 0.72) also trended towards an accuracy advantage over the ICH score (p = 0.09) and FUNC score (p = 0.03). In an analysis that excluded patients for whom comfort care was recommended, the 65 available attending physician predictions retained greater accuracy (r = 0.73) than either the ICH score (r = 0.50, p = 0.02) or the FUNC score (r = 0.42, p = 0.004). CONCLUSIONS: Early subjective clinical judgment of physicians correlates more closely with 3-month outcome after ICH than prognostic scales.
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    Complications associated with surgical treatment of traumatic spinal fractures: a review of the scoliosis research society morbidity and mortality database.
    (World neurosurgery, 2014-05) Williams, Brian J; Smith, Justin S; Saulle, Dwight; Ames, Christopher P; Lenke, Lawrence G; Broadstone, Paul A; Vaccaro, Alexander R; Polly, David W; Shaffrey, Christopher I

    Objective

    Traumatic spinal fracture is a common indication for surgery, with an associated high incidence of perioperative complications. The literature provides a wide range in the incidence of complications. We seek to assess the perioperative morbidity and mortality of surgery for traumatic spinal fractures and to identify predictors of their occurrence.

    Methods

    We performed a retrospective analysis of all traumatic spinal fracture cases submitted by members of the Scoliosis Research Society from 2004 to 2007.

    Results

    A total of 108,478 cases were submitted from 2004 through 2007, with 6,706 (6.2%) performed for treatment of traumatic fracture. Twenty-two percent of patients had preoperative neurological deficits. Intraoperative neuromonitoring was used in 58% of cases. The overall incidence of complications was 6.9%. The perioperative mortality was 0.5%. There were 59 (0.9%) new postoperative neurological deficits. Multivariate analysis demonstrated preoperative neurological deficit (P = .001; odds ratio [OR] 1.449, 95% confidence interval [CI] [1.156 to 1.817]) and fusion (P =.001; OR 1.12, 95% CI [1.072 to 1.168]) as predictors of complications and use of intraoperative neuromonitoring (P = .016; OR 1.949, 95% CI [1.13 to 3.361]), and preoperative neurological deficit (P < .001; OR 2.964, 95% CI [1.667 to 5.271]) as predictors of new postoperative neurological deficits (P < .001).

    Conclusions

    Overall, surgery for the treatment of spinal fractures was performed with relatively low incidences of perioperative complications (6.9%) and mortality (0.5%). These data may prove useful for patient counseling and ongoing efforts to improve the safety of operative care for patients with spinal fracture.
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    Development and implementation of a proficiency testing program for Luminex bead-based cytokine assays.
    (Journal of Immunological Methods, 2014-07) Lynch, Heather E; Sanchez, Ana M; D'Souza, M Patricia; Rountree, Wes; Denny, Thomas N; Kalos, Michael; Sempowski, Gregory D
    Luminex bead array assays are widely used for rapid biomarker quantification due to the ability to measure up to 100 unique analytes in a single well of a 96-well plate. There has been, however, no comprehensive analysis of variables impacting assay performance, nor development of a standardized proficiency testing program for laboratories performing these assays. To meet this need, the NIH/NIAID and the Cancer Immunotherapy Consortium of the Cancer Research Institute collaborated to develop and implement a Luminex assay proficiency testing program as part of the NIH/NIAID-sponsored External Quality Assurance Program Oversight Laboratory (EQAPOL) at Duke University. The program currently monitors 25 domestic and international sites with two external proficiency panels per year. Each panel includes a de-identified commercial Luminex assay kit with standards to quantify human IFNγ, TNFα, IL-6, IL-10 and IL-2, and a series of recombinant cytokine-spiked human serum samples. All aspects of panel development, testing and shipping are performed under GCLP by EQAPOL support teams. Following development testing, a comprehensive site proficiency scoring system comprised of timeliness, protocol adherence, accuracy and precision was implemented. The overall mean proficiency score across three rounds of testing has remained stable (EP3: 76%, EP4: 75%, EP5: 77%); however, a more detailed analysis of site reported results indicates a significant improvement of intra- (within) and inter- (between) site variation, suggesting that training and remediation for poor performing sites may be having a positive impact on proficiency. Through continued proficiency testing, identification of variables affecting Luminex assay outcomes will strengthen efforts to bring standardization to the field.
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    Development and Validation of Cervical Prediction Models for Patient-Reported Outcomes at 1 Year After Cervical Spine Surgery for Radiculopathy and Myelopathy.
    (Spine, 2020-11) Archer, Kristin R; Bydon, Mohamad; Khan, Inamullah; Nian, Hui; Pennings, Jacquelyn S; Harrell, Frank E; Sivaganesan, Ahilan; Chotai, Silky; McGirt, Matthew J; Foley, Kevin T; Glassman, Steven D; Mummaneni, Praveen V; Bisson, Erica F; Knightly, John J; Shaffrey, Christopher I; Asher, Anthony L; Devin, Clinton J; QOD Vanguard sites

    Study design

    Retrospective analysis of prospectively collected registry data.

    Objective

    To develop and validate prediction models for 12-month patient-reported outcomes of disability, pain, and myelopathy in patients undergoing elective cervical spine surgery.

    Summary of background data

    Predictive models have the potential to be utilized preoperatively to set expectations, adjust modifiable characteristics, and provide a patient-centered model of care.

    Methods

    This study was conducted using data from the cervical module of the Quality Outcomes Database. The outcomes of interest were disability (Neck Disability Index:), pain (Numeric Rating Scale), and modified Japanese Orthopaedic Association score for myelopathy. Multivariable proportional odds ordinal regression models were developed for patients with cervical radiculopathy and myelopathy. Patient demographic, clinical, and surgical covariates as well as baseline patient-reported outcomes scores were included in all models. The models were internally validated using bootstrap resampling to estimate the likely performance on a new sample of patients.

    Results

    Four thousand nine hundred eighty-eight patients underwent surgery for radiculopathy and 2641 patients for myelopathy. The most important predictor of poor postoperative outcomes at 12-months was the baseline Neck Disability Index score for patients with radiculopathy and modified Japanese Orthopaedic Association score for patients with myelopathy. In addition, symptom duration, workers' compensation, age, employment, and ambulatory and smoking status had a statistically significant impact on all outcomes (P < 0.001). Clinical and surgical variables contributed very little to predictive models, with posterior approach being associated with higher odds of having worse 12-month outcome scores in both the radiculopathy and myelopathy cohorts (P < 0.001). The full models overall discriminative performance ranged from 0.654 to 0.725.

    Conclusions

    These predictive models provide individualized risk-adjusted estimates of 12-month disability, pain, and myelopathy outcomes for patients undergoing spine surgery for degenerative cervical disease. Predictive models have the potential to be used as a shared decision-making tool for evidence-based preoperative counselling.

    Level of evidence

    2.