scholarly journals A Prediction Model for Severe AKI in Critically Ill Adults That Incorporates Clinical and Biomarker Data

2019 ◽  
Vol 14 (4) ◽  
pp. 506-514 ◽  
Author(s):  
Pavan Kumar Bhatraju ◽  
Leila R. Zelnick ◽  
Ronit Katz ◽  
Carmen Mikacenic ◽  
Susanna Kosamo ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Prediction Models ◽  
Risk Model ◽  
External Validation ◽  
Variable Model ◽  
Internal Validation ◽  
Predictive Values ◽  
Derivation Cohort ◽  
C Statistic

Background and objectivesCritically ill patients with worsening AKI are at high risk for poor outcomes. Predicting which patients will experience progression of AKI remains elusive. We sought to develop and validate a risk model for predicting severe AKI within 72 hours after intensive care unit admission.Design, setting, participants, & measurementsWe applied least absolute shrinkage and selection operator regression methodology to two prospectively enrolled, critically ill cohorts of patients who met criteria for the systemic inflammatory response syndrome, enrolled within 24–48 hours after hospital admission. The risk models were derived and internally validated in 1075 patients and externally validated in 262 patients. Demographics and laboratory and plasma biomarkers of inflammation or endothelial dysfunction were used in the prediction models. Severe AKI was defined as Kidney Disease Improving Global Outcomes (KDIGO) stage 2 or 3.ResultsSevere AKI developed in 62 (8%) patients in the derivation, 26 (8%) patients in the internal validation, and 15 (6%) patients in the external validation cohorts. In the derivation cohort, a three-variable model (age, cirrhosis, and soluble TNF receptor-1 concentrations [ACT]) had a c-statistic of 0.95 (95% confidence interval [95% CI], 0.91 to 0.97). The ACT model performed well in the internal (c-statistic, 0.90; 95% CI, 0.82 to 0.96) and external (c-statistic, 0.93; 95% CI, 0.89 to 0.97) validation cohorts. The ACT model had moderate positive predictive values (0.50–0.95) and high negative predictive values (0.94–0.95) for severe AKI in all three cohorts.ConclusionsACT is a simple, robust model that could be applied to improve risk prognostication and better target clinical trial enrollment in critically ill patients with AKI.

Abstract 17185: Modest Performance of Heart Failure Clinical Prediction Models: A Systematic External Validation Study

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Jenica N Upshaw ◽  
Jason Nelson ◽  
Benjamin Wessler ◽  
Benjamin Koethe ◽  
Christine Lundquist ◽  
...  
Keyword(s):  
Heart Failure ◽  
Prediction Models ◽  
External Validation ◽  
Significant Heterogeneity ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Derivation Cohort ◽  
C Statistic ◽  
Improved Model ◽  
Relative Change

Introduction: Most heart failure (HF) clinical prediction models (CPMs] have not been independently externally validated. We sought to test the performance of HF models in a diverse population using a systematic approach. Methods: A systematic review identified CPMs predicting outcomes for patients with HF. Individual patient data from 5 large publicaly available clinical trials enrolling patients with chronic HF were matched to published CPMs based on similarity in populations and available outcome and predictor variables in the clinical trial databases. CPM performance was evaluated for discrimination (c-statistic, % relative change in c-statistic) and calibration (Harrell’s E and E 90 , the mean and the 90% quantile of the error distribution from the smoothed loess observed value) for the original and recalibrated models. Results: Out of 135 HF CPMs reviewed, we identified 45 CPM-trial pairs including 13 unique CPMs. The outcome was mortality for all of the models with a trial match. During external validations, median c-statistic was 0.595 (IQR 0.563 to 0.630) with a median relative decrease in the c-statistic of -57 % (IQR, -49% to -71%) compared to the c-statistic reported in the derivation cohort. Overall, the median Harrell’s E was 0.09 (IQR, 0.04 to 0.135) and E 90 was 0.11 (IQR, 0.07 to 0.21). Recalibration of the intercept and slope led to substantially improved calibration with median change in Harrell’s E of -35% [IQR 0 to -75%] for the intercept and -56% [IQR -17% to -75%] for the intercept and slope. Refitting model covariates improved the median c-statistic by 38% to 0.629 [IQR 0.613 to 0.649]. Conclusion: For HF CPMs, independent external validations demonstrate that CPMs perform significantly worse than originally presented; however with significant heterogeneity. Recalibration of the intercept and slope improved model calibration. These results underscore the need to carefully consider the derivation cohort characteristics when using published CPMs.

External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy

Stroke ◽  
2021 ◽  
Author(s):  
Michiel H.F. Poorthuis ◽  
Reinier A.R. Herings ◽  
Kirsten Dansey ◽  
Johanna A.A. Damen ◽  
Jacoba P. Greving ◽  
...  
Keyword(s):  
Carotid Endarterectomy ◽  
Prediction Models ◽  
Risk Model ◽  
External Validation ◽  
The United States ◽  
Net Benefit ◽  
Improvement Program ◽  
C Statistic ◽  
Asymptomatic Patients ◽  
Number Of Patients

Background and Purpose: The net benefit of carotid endarterectomy (CEA) is determined partly by the risk of procedural stroke or death. Current guidelines recommend CEA if 30-day risks are <6% for symptomatic stenosis and <3% for asymptomatic stenosis. We aimed to identify prediction models for procedural stroke or death after CEA and to externally validate these models in a large registry of patients from the United States. Methods: We conducted a systematic search in MEDLINE and EMBASE for prediction models of procedural outcomes after CEA. We validated these models with data from patients who underwent CEA in the American College of Surgeons National Surgical Quality Improvement Program (2011–2017). We assessed discrimination using C statistics and calibration graphically. We determined the number of patients with predicted risks that exceeded recommended thresholds of procedural risks to perform CEA. Results: After screening 788 reports, 15 studies describing 17 prediction models were included. Nine were developed in populations including both asymptomatic and symptomatic patients, 2 in symptomatic and 5 in asymptomatic populations. In the external validation cohort of 26 293 patients who underwent CEA, 702 (2.7%) developed a stroke or died within 30-days. C statistics varied between 0.52 and 0.64 using all patients, between 0.51 and 0.59 using symptomatic patients, and between 0.49 to 0.58 using asymptomatic patients. The Ontario Carotid Endarterectomy Registry model that included symptomatic status, diabetes, heart failure, and contralateral occlusion as predictors, had C statistic of 0.64 and the best concordance between predicted and observed risks. This model identified 4.5% of symptomatic and 2.1% of asymptomatic patients with procedural risks that exceeded recommended thresholds. Conclusions: Of the 17 externally validated prediction models, the Ontario Carotid Endarterectomy Registry risk model had most reliable predictions of procedural stroke or death after CEA and can inform patients about procedural hazards and help focus CEA toward patients who would benefit most from it.

scholarly journals Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254550
Author(s):  
Matteo Luigi Giuseppe Leoni ◽  
Luisa Lombardelli ◽  
Davide Colombi ◽  
Elena Giovanna Bignami ◽  
Benedetta Pergolotti ◽  
...  
Keyword(s):  
Prediction Model ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Goodness Of Fit ◽  
Cox Regression ◽  
Prognostic Models ◽  
Calibration Plot ◽  
Internal Validation ◽  
Icu Admission ◽  
C Statistic

Background COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. Materials and methods We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model’s discriminatory ability was assessed with Harrell’s C-statistic and the goodness-of-fit was evaluated with calibration plot. Results 242 patients were included [median age, 64 years (56–71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6–18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82). Conclusions We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

scholarly journals Prediction models for acute kidney injury in critically ill patients: a protocol for systematic review and critical appraisal

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e046274
Author(s):  
Danqiong Wang ◽  
Weiwen Zhang ◽  
Jian Luo ◽  
Honglong Fang ◽  
Shanshan Jing ◽  
...  
Keyword(s):  
Systematic Review ◽  
Acute Kidney Injury ◽  
Critical Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Critical Appraisal ◽  
Prediction Models ◽  
External Validation ◽  
Kidney Injury ◽  
Risk Of Bias

IntroductionAcute kidney injury (AKI) has high morbidity and mortality in intensive care units, which can lead to chronic kidney disease, more costs and longer hospital stay. Early identification of AKI is crucial for clinical intervention. Although various risk prediction models have been developed to identify AKI, the overall predictive performance varies widely across studies. Owing to the different disease scenarios and the small number of externally validated cohorts in different prediction models, the stability and applicability of these models for AKI in critically ill patients are controversial. Moreover, there are no current risk-classification tools that are standardised for prediction of AKI in critically ill patients. The purpose of this systematic review is to map and assess prediction models for AKI in critically ill patients based on a comprehensive literature review.Methods and analysisA systematic review with meta-analysis is designed and will be conducted according to the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Three databases including PubMed, Cochrane Library and EMBASE from inception through October 2020 will be searched to identify all studies describing development and/or external validation of original multivariable models for predicting AKI in critically ill patients. Random-effects meta-analyses for external validation studies will be performed to estimate the performance of each model. The restricted maximum likelihood estimation and the Hartung-Knapp-Sidik-Jonkman method under a random-effects model will be applied to estimate the summary C statistic and 95% CI. 95% prediction interval integrating the heterogeneity will also be calculated to pool C-statistics to predict a possible range of C-statistics of future validation studies. Two investigators will extract data independently using the CHARMS checklist. Study quality or risk of bias will be assessed using the Prediction Model Risk of Bias Assessment Tool.Ethics and disseminationEthical approval and patient informed consent are not required because all information will be abstracted from published literatures. We plan to share our results with clinicians and publish them in a general or critical care medicine peer-reviewed journal. We also plan to present our results at critical care international conferences.OSF registration number10.17605/OSF.IO/X25AT.

An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

2021 ◽  
Author(s):  
Shaoxu Wu ◽  
Xiong Chen ◽  
Jiexin Pan ◽  
Wen Dong ◽  
Xiayao Diao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bladder Cancer ◽  
Diagnostic Accuracy ◽  
External Validation ◽  
Operation Time ◽  
Short Latency ◽  
Lesion Detection ◽  
Diagnostic Study ◽  
Internal Validation ◽  
Predictive Values

Abstract Background Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. Methods In total, 69,204 images from 10,729 consecutive patients from six hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. Results The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974–0.979) in the internal validation set and 0.990 (95% CI = 0.979–0.996), 0.982 (95% CI = 0.974–0.988), 0.978 (95% CI = 0.959–0.989), and 0.991 (95% CI = 0.987–0.994) in different external validation sets. In the CAIDS versus urologists’ comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902–0.964; and sensitivity = 0.954, 95% CI = 0.902–0.983) with a short latency of 12 s, much more accurate and quicker than the expert urologists. Conclusions The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.

scholarly journals Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shubhayu Bhattacharyay ◽  
John Rattray ◽  
Matthew Wang ◽  
Peter H. Dziedzic ◽  
Eusebia Calvillo ◽  
...  
Keyword(s):  
Brain Injury ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
High Frequency ◽  
Prediction Models ◽  
Severe Brain Injury ◽  
Short Term ◽  
Time Frequency ◽  
Yield Information ◽  
Functional States

AbstractOur goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, we recorded a median 24.1 (IQR: 22.8–25.1) hours of high-frequency accelerometry data per patient from a prospective cohort (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at discharge, measured with the Glasgow Outcome Scale–Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53–0.85]) and consistent (observation windows: 12 min–9 h) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated patients of upper moderate disability or better (GOSE > 5) with 2–6 h of observation (AUC: 0.82 [95% CI: 0.75–0.90]). Results suggest that time series analysis of motor activity yields clinically relevant insights on underlying functional states and short-term outcomes in patients with SBI.

scholarly journals The Roth score as a triage tool for detecting hypoxaemia in general practice: a diagnostic validation study in patients with possible COVID-19

2021 ◽  
Vol 22 ◽  
Author(s):  
Charlotte E.M. ten Broeke ◽  
Jelle C.L. Himmelreich ◽  
Jochen W.L. Cals ◽  
Wim A.M. Lucassen ◽  
Ralf E. Harskamp
Keyword(s):  
General Practice ◽  
Validation Study ◽  
Virus Disease ◽  
External Validation ◽  
Added Value ◽  
Cross Sectional ◽  
Predictive Values ◽  
Single Breath ◽  
C Statistic ◽  
Counting Time

Abstract Aim: To validate the Roth score as a triage tool for detecting hypoxaemia. Backgrounds: The virtual assessment of patients has become increasingly important during the corona virus disease (COVID-19) pandemic, but has limitations as to the evaluation of deteriorating respiratory function. This study presents data on the validity of the Roth score as a triage tool for detecting hypoxaemia remotely in potential COVID-19 patients in general practice. Methods: This cross-sectional validation study was conducted in Dutch general practice. Patients aged ≥18 with suspected or confirmed COVID-19 were asked to rapidly count from 1 to 30 in a single breath. The Roth score involves the highest number counted during exhalation (counting number) and the time taken to reach the maximal count (counting time). Outcome measures were (1) the correlation between both Roth score measurements and simultaneous pulse oximetry (SpO2) on room air and (2) discrimination (c-statistic), sensitivity, specificity and predictive values of the Roth score for detecting hypoxaemia (SpO2 < 95%). Findings: A total of 33 physicians enrolled 105 patients (52.4% female, mean age of 52.6 ± 20.4 years). A positive correlation was found between counting number and SpO2 (rs = 0.44, P < 0.001), whereas only a weak correlation was found between counting time and SpO2 (rs = 0.15, P = 0.14). Discrimination for hypoxaemia was higher for counting number [c-statistic 0.91 (95% CI: 0.85–0.96)] than for counting time [c-statistic 0.77 (95% CI: 0.62–0.93)]. Optimal diagnostic performance was found at a counting number of 20, with a sensitivity of 93.3% (95% CI: 68.1–99.8) and a specificity of 77.8% (95% CI: 67.8–85.9). A counting time of 7 s showed the best sensitivity of 85.7% (95% CI: 57.2–98.2) and specificity of 81.1% (95% CI: 71.5–88.6). Conclusions: A Roth score, with an optimal counting number cut-off value of 20, maybe of added value for signalling hypoxaemia in general practice. Further external validation is warranted before recommending integration in telephone triage.

Development and validation of a medication regimen complexity scoring tool for critically ill patients

2019 ◽  
Vol 76 (Supplement_2) ◽  
pp. S34-S40 ◽  
Author(s):  
Morgan E Gwynn ◽  
Margaret O Poisson ◽  
Jennifer L Waller ◽  
Andrea Sikora Newsome
Keyword(s):  
Length Of Stay ◽  
Critically Ill ◽  
Patient Outcomes ◽  
Critically Ill Patients ◽  
External Validation ◽  
Medication Regimen ◽  
Patient Acuity ◽  
Medication Regimen Complexity ◽  
Regimen Complexity ◽  
Scoring Tool

Abstract Purpose The purpose of this study was to develop and validate a novel medication regimen complexity–intensive care unit (MRC-ICU) scoring tool in critically ill patients and to correlate MRC with illness severity and patient outcomes. Methods This study was a single-center, retrospective observational chart review of adults admitted to the medical ICU (MICU) between November 2016 and June 2017. The primary aim was the development and internal validation of the MRC-ICU scoring tool. Secondary aims included external validation of the MRC-ICU and exploration of relationships between medication regimen complexity and patient outcomes. Exclusion criteria included a length of stay of less than 24 hours in the MICU, active transfer, or hospice orders at 24 hours. A total of 130 patient medication regimens were used to test, modify, and validate the MRC-ICU tool. Results The 39-line item medication regimen complexity scoring tool was validated both internally and externally. Convergent validity was confirmed with total medications (p < 0.0001). Score discriminant validity was confirmed by lack of association with age (p = 0.1039) or sex (p = 0.7829). The MRC-ICU score was significantly associated with ICU length of stay (p = 0.0166), ICU mortality (p = 0.0193), and patient acuity (p < 0.0001). Conclusion The MRC-ICU scoring tool was validated and found to correlate with length of stay, inpatient mortality, and patient acuity.

scholarly journals Model-Based Quantification of Left Ventricular Diastolic Function in Critically Ill Patients with Atrial Fibrillation from Routine Data: A Feasibility Study

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Nicholas Kiefer ◽  
Maximilian J. Oremek ◽  
Andreas Hoeft ◽  
Sven Zenker
Keyword(s):  
Atrial Fibrillation ◽  
Diastolic Function ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Likelihood Function ◽  
External Validation ◽  
Left Ventricular ◽  
Monitoring Data ◽  
Diastolic Filling ◽  
Physiological Range

Introduction. Left ventricular diastolic dysfunction (LVDD) and atrial fibrillation (AF) are connected by pathophysiology and prevalence. LVDD remains underdiagnosed in critically ill patients despite potentially significant therapeutic implications since direct measurement cannot be performed in routine care at the bedside, and echocardiographic assessment of LVDD in AF is impaired. We propose a novel approach that allows us to infer the diastolic stiffness, β, a key quantitative parameter of diastolic function, from standard monitoring data by solving the nonlinear, ill-posed inverse problem of parameter estimation for a previously described mechanistic, physiological model of diastolic filling. The beat-to-beat variability in AF offers an advantageous setting for this. Methods. By employing a global optimization algorithm, β is inferred from a simple six parameter and an expanded seven parameter model of left ventricular filling. Optimization of all parameters was limited to the interval ]0, 400[ and initialized randomly on large intervals encompassing the support of the likelihood function. Routine ECG and arterial pressure recordings of 17 AF and 3 sinus rhythm (SR) patients from the PhysioNet MGH/MF Database were used as inputs. Results. Estimation was successful in 15 of 17 AF patients, while in the 3 SR patients, no reliable estimation was possible. For both models, the inferred β (0.065 ± 0.044 ml−1 vs. 0.038 ± 0.033 ml−1 (p=0.02) simple vs. expanded) was compatible with the previously described (patho) physiological range. Aortic compliance, α, inferred from the expanded model (1.46 ± 1.50 ml/mmHg) also compared well with literature values. Conclusion. The proposed approach successfully inferred β within the physiological range. This is the first report of an approach quantifying LVDF from routine monitoring data in critically ill AF patients. Provided future successful external validation, this approach may offer a tool for minimally invasive online monitoring of this crucial parameter.

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