scholarly journals Derivation and External Validation of a Simple Prediction Rule for the Development of Respiratory Failure in Hospitalized Patients With Influenza

2022 ◽  
Author(s):  
Blanca Ayuso ◽  
Antonio Lalueza ◽  
Estibaliz Arrieta ◽  
Eva Maria Romay ◽  
Álvaro Marchán-López ◽  
...  
Keyword(s):  
Respiratory Failure ◽  
Lactate Dehydrogenase ◽  
Respiratory Rate ◽  
Validation Cohort ◽  
Influenza Infection ◽  
External Validation ◽  
Prediction Rule ◽  
Influenza Viruses ◽  
Chronic Obstructive ◽  
Derivation Cohort

Abstract BACKGROUND: Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients.METHODS: a prospective cohort study was conducted during two consecutive Influenza seasons (December 2016 - March 2017 and December 2017 - April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 - May 2019. RESULTS: Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p<0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher’s test p>0.43). CONCLUSION: we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient´s stratification during seasonal Influenza epidemics.

Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening

2021 ◽  
pp. 2003386
Author(s):  
Anton Schreuder ◽  
Colin Jacobs ◽  
Nikolas Lessmann ◽  
Mireille JM Broeders ◽  
Mario Silva ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cox Regression ◽  
Validation Cohort ◽  
External Validation ◽  
Patient Characteristics ◽  
Chronic Obstructive ◽  
Final Model ◽  
Derivation Cohort ◽  
Copd Mortality ◽  
Cvd Mortality

ObjectivesCombined assessment of cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), and lung cancer (LC) may improve the effectiveness of LC screening in smokers. The aims were to derive and assess risk models for predicting LC incidence, CVD mortality, and COPD mortality by combining quantitative CT measures from each disease, and to quantify the added predictive benefit of self-reported patient characteristics given the availability of a CT scan.MethodsA survey model (patient characteristics only), CT model (CT information only), and final model (all variables) were derived for each outcome using parsimonious Cox regression on a sample from the National Lung Screening Trial (n=15 000). Validation was performed using Multicentric Italian Lung Detection data (n=2287). Time-dependent measures of model discrimination and calibration are reported.ResultsAge, mean lung density, emphysema score, bronchial wall thickness, and aorta calcium volume are variables which contributed to all final models. Nodule features were crucial for LC incidence predictions but did not contribute to CVD and COPD mortality prediction. In the derivation cohort, the LC incidence CT model had a 5-year area under the receiver operating characteristic curve (AUC) of 82·5% (95% confidence interval=80·9–84·0%), significantly inferior to that of the final model (84·0%, 82·6–85·5%). However, the addition of patient characteristics did not improve the LC incidence model performance in the validation cohort (CT model=80·1%, 74·2–86·0%; final model=79·9, 73·9–85·8%). Similarly, the final CVD mortality model outperformed the other two models in the derivation cohort (survey model=74·9%, 72·7–77·1%; CT model=76·3%, 74·1–78·5%; final model=79·1%, 77·0–81·2%) but not the validation cohort (survey model=74·8%, 62·2–87·5%; CT model=72·1%, 61·1–83·2%; final model=72·2%, 60·4–84·0%). Combining patient characteristics and CT measures provided the largest increase in accuracy for the COPD mortality final model (92·3%, 90·1–94·5%) compared to either other model individually (survey model=87·5%, 84·3–90·6%; CT model=87·9%, 84·8–91·0%), but no external validation was performed due to a very low event frequency.ConclusionsCT measures of CVD and COPD provides small but reproducible improvements to nodule-based LC risk prediction accuracy from 3 years’ onwards. Self-reported patient characteristics may not be of added predictive value when CT information is available.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

scholarly journals Prediction model of bleeding after endoscopic submucosal dissection for early gastric cancer: BEST-J score

Gut ◽  
2020 ◽  
pp. gutjnl-2019-319926 ◽  
Author(s):  
Waku Hatta ◽  
Yosuke Tsuji ◽  
Toshiyuki Yoshio ◽  
Naomi Kakushima ◽  
Shu Hoteya ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Early Gastric Cancer ◽  
Prediction Model ◽  
Endoscopic Submucosal Dissection ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Derivation Cohort ◽  
Submucosal Dissection

ObjectiveBleeding after endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) is a frequent adverse event after ESD. We aimed to develop and externally validate a clinically useful prediction model (BEST-J score: Bleeding after ESD Trend from Japan) for bleeding after ESD for EGC.DesignThis retrospective study enrolled patients who underwent ESD for EGC. Patients in the derivation cohort (n=8291) were recruited from 25 institutions, and patients in the external validation cohort (n=2029) were recruited from eight institutions in other areas. In the derivation cohort, weighted points were assigned to predictors of bleeding determined in the multivariate logistic regression analysis and a prediction model was established. External validation of the model was conducted to analyse discrimination and calibration.ResultsA prediction model comprised 10 variables (warfarin, direct oral anticoagulant, chronic kidney disease with haemodialysis, P2Y12 receptor antagonist, aspirin, cilostazol, tumour size >30 mm, lower-third in tumour location, presence of multiple tumours and interruption of each kind of antithrombotic agents). The rates of bleeding after ESD at low-risk (0 to 1 points), intermediate-risk (2 points), high-risk (3 to 4 points) and very high-risk (≥5 points) were 2.8%, 6.1%, 11.4% and 29.7%, respectively. In the external validation cohort, the model showed moderately good discrimination, with a c-statistic of 0.70 (95% CI, 0.64 to 0.76), and good calibration (calibration-in-the-large, 0.05; calibration slope, 1.01).ConclusionsIn this nationwide multicentre study, we derived and externally validated a prediction model for bleeding after ESD. This model may be a good clinical decision-making support tool for ESD in patients with EGC.

scholarly journals Machine learning identifies clinical parameters to predict mortality in patients undergoing transcatheter mitral valve repair

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
E Zweck ◽  
M Spieker ◽  
P Horn ◽  
C Iliadis ◽  
C Metze ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Mitral Valve ◽  
Risk Stratification ◽  
Mitral Valve Repair ◽  
Validation Cohort ◽  
External Validation ◽  
Valve Repair ◽  
Derivation Cohort ◽  
Transcatheter Mitral Valve Repair

Abstract Background Transcatheter Mitral Valve Repair (TMVR) with MitraClip is an important treatment option for patients with severe mitral regurgitation. The lack of appropriate, validated and specific means to risk stratify TMVR patients complicates the evaluation of prognostic benefits of TMVR in clinical trials and practice. Purpose We aimed to develop an optimized risk stratification model for TMVR patients using machine learning (ML). Methods We included a total of 1009 TMVR patients from three large university hospitals, of which one (n=317) served as an external validation cohort. The primary endpoint was all-cause 1-year mortality, which was known in 95% of patients. Model performance was assessed using receiver operating characteristics. In the derivation cohort, different ML algorithms, including random forest, logistic regression, support vectors machines, k nearest neighbors, multilayer perceptron, and extreme gradient boosting (XGBoost) were tested using 5-fold cross-validation in the derivation cohort. The final model (Transcatheter MITral Valve Repair MortALIty PredicTion SYstem; MITRALITY) was tested in the validation cohort with respect to existing clinical scores. Results XGBoost was selected as the final algorithm for the MITRALITY Score, using only six baseline clinical features for prediction (in order of predictive importance): blood urea nitrogen, hemoglobin, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), mean arterial pressure, body mass index, and creatinine. In the external validation cohort, the MITRALITY Score's area under the curve (AUC) was 0.783, outperforming existing scores which yielded AUCs of 0.721 and 0.657 at best. 1-year mortality in the MITRALITY Score quartiles across the total cohort was 0.8%, 1.3%, 10.5%, and 54.6%, respectively. Odds of mortality in MITRALITY Score quartile 4 as compared to quartile 1 were 143.02 [34.75; 588.57]. Survival analyses showed that the differences in outcomes between the MITRALITY Score quartiles remained even over a timeframe of 3 years post intervention (log rank: p&lt;0.005). With each increase by 1% in the MITRALITY score, the respective proportional hazard ratio for 3-year survival was 1.06 [1.05, 1.07] (Cox regression, p&lt;0.05). Conclusion The MITRALITY Score is a novel, internally and externally validated ML-based tool for risk stratification of patients prior to TMVR. These findings may potentially allow for more precise design of future clinical trials, may enable novel treatment strategies tailored to populations of specific risk and thereby serve future daily clinical practice. FUNDunding Acknowledgement Type of funding sources: None. Summary Figure

scholarly journals Development and external validation of a prognostic tool for COVID-19 critical disease

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242953
Author(s):  
Daniel S. Chow ◽  
Justin Glavis-Bloom ◽  
Jennifer E. Soun ◽  
Brent Weinberg ◽  
Theresa Berens Loveless ◽  
...  
Keyword(s):  
Critical Care ◽  
Prognostic Model ◽  
Validation Cohort ◽  
External Validation ◽  
Vital Signs ◽  
Clinical Tool ◽  
Derivation Cohort ◽  
Laboratory Values ◽  
Increased Risk ◽  
Care Capacity

Background The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. Methods This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. Results Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21–88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27–88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87–1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease. Conclusions and relevance We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.

Host-Response Subphenotypic Classification with A Parsimonious Model Offers Prognostic Information in Patients with Acute Respiratory Failure: A Prospective Cohort Study.

2020 ◽  
Author(s):  
Callie M. Drohan ◽  
S. Mehdi Nouraie ◽  
William Bain ◽  
Faraaz A. Shah ◽  
John Evankovich ◽  
...  
Keyword(s):  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Host Response ◽  
Latent Class ◽  
Validation Cohort ◽  
Prognostic Information ◽  
Derivation Cohort ◽  
Parsimonious Model

Abstract Background: Recent research in patients with ARDS has consistently shown the presence of two distinct subphenotypes of host-responses (hyper- and hypo-inflammatory) with markedly different outcomes and responses to therapies. However, inherent uncertainty in reaching the diagnosis of ARDS creates considerable biological and clinical overlap with other broadly-defined syndromes of acute respiratory failure, such as patients with risk factors (e.g. sepsis or pneumonia) for ARDS (at-risk for ARDS [ARFA]) or patients with decompensated congestive heart failure (CHF). Limited data are available for the presence of subphenotypes in such broader critically-ill populations. Methods: We enrolled mechanically-ventilated patients with acute respiratory failure (ARDS, ARFA, and CHF) and measured 11 plasma biomarkers at baseline. We applied latent class analysis (LCA) methods to determine optimal subphenotypic classifications in this inclusive patient cohort by considering clinical variables and biomarkers. We then derived a parsimonious logistic regression model for subphenotype predictions and compared clinical outcomes between subphenotypes.Results: We included 334 patients (123 [37%] ARDS, 177 [53%] ARFA, 34 [10%] CHF) in a derivation cohort and 36 patients in a temporally-independent validation cohort. A two-class LCA model was found to be optimal, classifying 29% of patients in the hyper-inflammatory subphenotype, consistent with prior findings. A 4-variable parsimonious model (angiopoietin-2, soluble tumor necrosis factor receptor-1, procalcitonin and bicarbonate) for subphenotype prediction offered excellent classification (area under the curve = 0.98) compared to LCA classifications. For both LCA- and regression model classifications, hyper-inflammatory patients had higher severity of illness by Sequential Organ Failure Assessment scores, fewer ventilator-free days and higher 30- and 90-day mortality (all p<0.01) compared to the hypo-inflammatory group. Subphenotype predictions in the validation cohort revealed consistent trends for clinical outcomes and higher levels of inflammatory biomarkers in the hyper-inflammatory group (22%). Conclusions: Host-response subphenotypes are observable in broader and heterogeneous patient populations beyond just patients with ARDS, and subphenotypic classifications offer prognostic information on clinical outcomes. Accurate subphenotyping is possible with the use of a simple predictive model to improve clinical applicability.

scholarly journals A Diagnostic Algorithm Based on a Simple Clinical Prediction Rule for the Diagnosis of Cranial Giant Cell Arteritis

2021 ◽  
Vol 10 (6) ◽  
pp. 1163
Author(s):  
Michael Czihal ◽  
Christian Lottspeich ◽  
Christoph Bernau ◽  
Teresa Henke ◽  
Ilaria Prearo ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Giant Cell Arteritis ◽  
Giant Cell ◽  
Validation Cohort ◽  
Prediction Rule ◽  
Diagnostic Algorithm ◽  
Clinical Prediction Rule ◽  
Ischemic Optic Neuropathy ◽  
Clinical Prediction ◽  
Derivation Cohort

Background: Risk stratification based on pre-test probability may improve the diagnostic accuracy of temporal artery high-resolution compression sonography (hrTCS) in the diagnostic workup of cranial giant cell arteritis (cGCA). Methods: A logistic regression model with candidate items was derived from a cohort of patients with suspected cGCA (n = 87). The diagnostic accuracy of the model was tested in the derivation cohort and in an independent validation cohort (n = 114) by receiver operator characteristics (ROC) analysis. The clinical items were composed of a clinical prediction rule, integrated into a stepwise diagnostic algorithm together with C-reactive protein (CRP) values and hrTCS values. Results: The model consisted of four clinical variables (age > 70, headache, jaw claudication, and anterior ischemic optic neuropathy). The diagnostic accuracy of the model for discrimination of patients with and without a final clinical diagnosis of cGCA was excellent in both cohorts (area under the curve (AUC) 0.96 and AUC 0.92, respectively). The diagnostic algorithm improved the positive predictive value of hrCTS substantially. Within the algorithm, 32.8% of patients (derivation cohort) and 49.1% (validation cohort) would not have been tested by hrTCS. None of these patients had a final diagnosis of cGCA. Conclusion: A diagnostic algorithm based on a clinical prediction rule improves the diagnostic accuracy of hrTCS.

scholarly journals Derivation and external validation of a risk prediction algorithm to estimate future risk of cardiovascular death among patients with type 2 diabetes and incident diabetic nephropathy: prospective cohort study

2019 ◽  
Vol 7 (1) ◽  
pp. e000735 ◽  
Author(s):  
Dahai Yu ◽  
Jin Shang ◽  
Yamei Cai ◽  
Zheng Wang ◽  
Xiaoxue Zhang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Nephropathy ◽  
Risk Score ◽  
Cardiovascular Mortality ◽  
Validation Cohort ◽  
External Validation ◽  
Cardiovascular Death ◽  
Individual Risk ◽  
Derivation Cohort

ObjectiveTo derive, and externally validate, a risk score for cardiovascular death among patients with type 2 diabetes and newly diagnosed diabetic nephropathy (DN).Research design and methodsTwo independent prospective cohorts with type 2 diabetes were used to develop and externally validate the risk score. The derivation cohort comprised 2282 patients with an incident, clinical diagnosis of DN. The validation cohort includes 950 patients with incident, biopsy-proven diagnosis of DN. The outcome was cardiovascular death within 2 years of the diagnosis of DN. Logistic regression was applied to derive the risk score for cardiovascular death from the derivation cohort, which was externally validated in the validation cohort. The score was also estimated by applying the United Kingdom Prospective Diabetes Study (UKPDS) risk score in the external validation cohort.ResultsThe 2-year cardiovascular mortality was 12.05% and 11.79% in the derivation cohort and validation cohort, respectively. Traditional predictors including age, gender, body mass index, blood pressures, glucose, lipid profiles alongside novel laboratory test items covering five test panels (liver function, serum electrolytes, thyroid function, blood coagulation and blood count) were included in the final model.C-statistics was 0.736 (95% CI 0.731 to 0.740) and 0.747 (95% CI 0.737 to 0.756) in the derivation cohort and validation cohort, respectively. The calibration slope was 0.993 (95% CI 0.974 to 1.013) and 1.000 (95% CI 0.981 to 1.020) in the derivation cohort and validation cohort, respectively.The UKPDS risk score substantially underestimated cardiovascular mortality.ConclusionsA new risk score based on routine clinical measurements that quantified individual risk of cardiovascular death was developed and externally validated. Compared with the UKPDS risk score, which underestimated the cardiovascular disease risk, the new score is a more specific tool for patients with type 2 diabetes and DN. The score could work as a tool to identify individuals at the highest risk of cardiovascular death among those with DN.

scholarly journals Simple Scoring Tool to Estimate Risk of Hospitalization and Mortality in Ambulatory and Emergency Department Patients with COVID-19

2021 ◽  
Author(s):  
Brandon J. Webb ◽  
Nicholas M. Levin ◽  
Nancy Grisel ◽  
Samuel M. Brown ◽  
Ithan D. Peltan ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
High Risk ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Receiver Operator Characteristic Curve ◽  
Multivariable Logistic Regression Model ◽  
White Race ◽  
Derivation Cohort ◽  
Scoring Tool

AbstractBackgroundAccurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality.MethodsAll consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality.Results22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n=16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n=6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9).ConclusionA prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

scholarly journals The NIV Outcomes (NIVO) Score: prediction of in-hospital mortality in exacerbations of COPD requiring assisted ventilation

2021 ◽  
pp. 2004042
Author(s):  
Tom Hartley ◽  
Nicholas D. Lane ◽  
John Steer ◽  
Mark W. Elliott ◽  
Milind P. Sovani ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Validation Cohort ◽  
A Priori ◽  
External Validation ◽  
Risk Groups ◽  
Assisted Ventilation ◽  
Hospital Death ◽  
Prediction Tool ◽  
Trial Methodology ◽  
Derivation Cohort

IntroductionAcute exacerbations of COPD (AECOPD) complicated by acute (acidaemic) hypercapnic respiratory failure (AHRF) requiring ventilation are common. When applied appropriately, ventilation substantially reduces mortality. Despite this, there is evidence of poor practice and prognostic pessimism. A clinical prediction tool could improve decision making regarding ventilation, but none is routinely used.MethodsConsecutive patients admitted with AECOPD and AHRF treated with assisted ventilation (principally non-invasive ventilation) were identified in two hospitals serving differing populations. Known and potential prognostic indices were identified a priori. A prediction tool for in-hospital death was derived using multivariable regression analysis. Prospective, external validation was performed in a temporally separate, geographically diverse 10-centre study. The trial methodology adhered to TRIPOD recommendations.ResultsDerivation cohort, n=489, in-hospital mortality 25.4%; validation cohort, n=733, in-hospital mortality 20.1%. Using 6 simple categorised variables; extended Medical Research Council Dyspnoea score (eMRCD)1–4/5a/5b, time from admission to acidaemia >12 h, pH<7.25, presence of atrial fibrillation, Glasgow coma scale ≤14 and chest radiograph consolidation a simple scoring system with strong prediction of in-hospital mortality is achieved. The resultant NIVO score had area under the receiver operated curve of 0.79 and offers good calibration and discrimination across stratified risk groups in its validation cohort.DiscussionThe NIVO score outperformed pre-specified comparator scores. It is validated in a generalisable cohort and works despite the heterogeneity inherent to both this patient group and this intervention. Potential applications include informing discussions with patients and their families, aiding treatment escalation decisions, challenging pessimism, and comparing risk-adjusted outcomes across centres.

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