Dataset Information

Predicting in-hospital mortality for initial survivors of acute respiratory compromise (ARC) events: Development and validation of the ARC Score.

ABSTRACT: Acute respiratory compromise (ARC) is a common and highly morbid event in hospitalized patients. To date, however, few investigators have explored predictors of outcome in initial survivors of ARC events. In the present study, we leveraged the American Heart Association's Get With The Guidelines®-Resuscitation (GWTG-R) ARC data registry to develop a prognostic score for initial survivors of ARC events.Using GWTG-R ARC data, we identified 13,193 index ARC events. These events were divided into a derivation cohort (9807 patients) and a validation cohort (3386 patients). A score for predicting in-hospital mortality was developed using multivariable modeling with generalized estimating equations.The two cohorts were well balanced in terms of baseline demographics, illness-types, pre-event conditions, event characteristics, and overall mortality. After model optimization, nine variables associated with the outcome of interest were included. Age, hypotension preceding the event, and intubation during the event were the greatest predictors of in-hospital mortality. The final score demonstrated good discrimination in both the derivation and validation cohorts. The score was also very well calibrated in both cohorts. Observed average mortality was <10% in the lowest score category of both cohorts and >70% in the highest category, illustrating a wide range of mortality separated effectively by the scoring system.In the present study, we developed and internally validated a prognostic score for initial survivors of in-hospital ARC events. This tool will be useful for clinical prognostication, selecting cohorts for interventional studies, and for quality improvement initiatives seeking to risk-adjust for hospital-to-hospital comparisons.

SUBMITTER: Moskowitz A

PROVIDER: S-EPMC5502129 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Predicting in-hospital mortality for initial survivors of acute respiratory compromise (ARC) events: Development and validation of the ARC Score.

Moskowitz Ari A Andersen Lars W LW Karlsson Mathias M Grossestreuer Anne V AV Chase Maureen M Cocchi Michael N MN Berg Katherine K Donnino Michael W MW

Resuscitation 20170304

<h4>Aim</h4>Acute respiratory compromise (ARC) is a common and highly morbid event in hospitalized patients. To date, however, few investigators have explored predictors of outcome in initial survivors of ARC events. In the present study, we leveraged the American Heart Association's Get With The Guidelines<sup>®</sup>-Resuscitation (GWTG-R) ARC data registry to develop a prognostic score for initial survivors of ARC events.<h4>Methods</h4>Using GWTG-R ARC data, we identified 13,193 index ARC ev ...[more]

PMID: 28267618

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Project description:BackgroundThe CARDOT scores have been developed for prediction of respiratory complications after thoracic surgery. This study aimed to externally validate the CARDOT score and assess the predictive value of preoperative neutrophil-to-lymphocyte ratio (NLR) for postoperative respiratory complication.MethodsA retrospective cohort study of consecutive thoracic surgical patients at a single tertiary hospital in northern Thailand was conducted. The development and validation datasets were collected between 2006 and 2012 and from 2015 to 2021, respectively. Six prespecified predictive factors were identified, and formed a predictive score, the CARDOT score (chronic obstructive pulmonary disease, American Society of Anesthesiologists physical status, right-sided operation, duration of surgery, preoperative oxygen saturation on room air, thoracotomy), was calculated. The performance of the CARDOT score was evaluated in terms of discrimination by using the area under the receiver operating characteristic (AuROC) curve and calibration.ResultsThere were 1086 and 1645 patients included in the development and validation datasets. The incidence of respiratory complications was 15.7% (171 of 1086) and 22.5% (370 of 1645) in the development and validation datasets, respectively. The CARDOT score had good discriminative ability for both the development and validation datasets (AuROC 0.789 (95% CI 0.753-0.827) and 0.758 (95% CI 0.730-0.787), respectively). The CARDOT score showed good calibration in both datasets. A high NLR (≥ 4.5) significantly increased the risk of respiratory complications after thoracic surgery (P < 0.001). The AuROC curve of the validation cohort increased to 0.775 (95% CI 0.750-0.800) when the score was combined with a high NLR. The AuROC of the CARDOT score with the NLR showed significantly greater discrimination power than that of the CARDOT score alone (P = 0.008).ConclusionsThe CARDOT score showed a good discriminative performance in the external validation dataset. An addition of a high NLR significantly increases the predictive performance of CARDOT score. The utility of this score is valuable in settings with limited access to preoperative pulmonary function testing.

Project description:Dynamic Wave Arc (DWA) is a clinical approach designed to maximize the versatility of Vero SBRT system by synchronizing the gantry-ring noncoplanar movement with D-MLC optimization. The purpose of this study was to verify the delivery accuracy of DWA approach and to evaluate the potential dosimetric benefits.DWA is an extended form of VMAT with a continuous varying ring position. The main difference in the optimization modules of VMAT and DWA is during the angular spacing, where the DWA algorithm does not consider the gantry spacing, but only the Euclidian norm of the ring and gantry angle. A preclinical version of RayStation v4.6 (RaySearch Laboratories, Sweden) was used to create patient specific wave arc trajectories for 31 patients with various anatomical tumor regions (prostate, oligometatstatic cases, centrally-located non-small cell lung cancer (NSCLC) and locally advanced pancreatic cancer-LAPC). DWA was benchmarked against the current clinical approaches and coplanar VMAT. Each plan was evaluated with regards to dose distribution, modulation complexity (MCS), monitor units and treatment time efficiency. The delivery accuracy was evaluated using a 2D diode array that takes in consideration the multi-dimensionality of DWA during dose reconstruction.In centrally-located NSCLC cases, DWA improved the low dose spillage with 20 %, while the target coverage was increased with 17 % compared to 3D CRT. The structures that significantly benefited from using DWA were proximal bronchus and esophagus, with the maximal dose being reduced by 17 % and 24 %, respectively. For prostate and LAPC, neither technique seemed clearly superior to the other; however, DWA reduced with more than 65 % of the delivery time over IMRT. A steeper dose gradient outside the target was observed for all treatment sites (p < 0.01) with DWA. Except the oligometastatic cases, where the DWA-MCSs indicate a higher modulation, both DWA and VMAT modalities provide plans of similar complexity. The average ? (3 % /3 mm) passing rate for DWA plans was 99.2 ± 1 % (range from 96.8 to 100 %).DWA proven to be a fully functional treatment technique, allowing additional flexibility in dose shaping, while preserving dosimetrically robust delivery and treatment times comparable with coplanar VMAT.

Project description:OBJECTIVES:To develop a clinical prediction score for predicting mortality in children following return of spontaneous circulation after in-hospital cardiac arrest. DESIGN:Observational study using prospectively collected data. SETTING:This was an analysis using data from the Get With The Guidelines-Resuscitation registry between January 2000 and December 2015. PATIENTS:Pediatric patients (< 18 yr old) who achieved return of spontaneous circulation. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The primary outcome was in-hospital mortality. Patients were divided into a derivation (3/4) and validation (1/4) cohort. A prediction score was developed using a multivariable logistic regression model with backward selection. Patient and event characteristics for the derivation cohort (n = 3,893) and validation cohort (n = 1,297) were similar. Seventeen variables associated with the outcome remained in the final reduced model after backward elimination. Predictors of in-hospital mortality included age, illness category, pre-event characteristics, arrest location, day of the week, nonshockable pulseless rhythm, duration of chest compressions, and interventions in place at time of arrest. The C-statistic for the final score was 0.77 (95% CI, 0.75-0.78) in the derivation cohort and 0.77 (95% CI, 0.74-0.79) in the validation cohort. The expected versus observed mortality plot indicated good calibration in both the derivation and validation cohorts. The score showed a stepwise increase in mortality with an observed mortality of less than 15% for scores 0-9 and greater than 80% for scores greater than or equal to 25. The model also performed well for neurologic outcome and in sensitivity analyses for events within the past 5 years and for patients with or without a pulse at the onset of chest compressions. CONCLUSIONS:We developed and internally validated a prediction score for initial survivors of pediatric in-hospital cardiac arrest. This prediction score may be useful for prognostication following cardiac arrest, stratifying patients for research, and guiding quality improvement initiatives.

Project description:ObjectiveTo develop and externally validate a risk algorithm (QAdmissions) to estimate the risk of emergency hospital admission for patients aged 18-100 years in primary care.DesignProspective open cohort study using routinely collected data from general practice linked to hospital episode data during the 2-year study period 1 January 2010 to 31 December 2011.Setting405 general practices in England contributing to the national QResearch database to develop the algorithm. Two validation cohorts to validate the algorithm (1) 202 different QResearch practices and (2) 343 practices in England contributing to the Clinical Practice Research DataLink (CPRD). All general practices had data linked to hospital episode statistics at the individual patient level.ParticipantsWe studied 2 849 381 patients aged 18-100 years in the derivation cohort with over 4.6 million person-years of follow-up. 265 573 of these patients had one or more emergency admissions during follow-up. For the QResearch validation cohort, we identified 1 340 622 patients aged 18-100 years with over 2.2 million person-years of follow-up. Of these patients, 132 723 had one or more emergency admissions during follow-up. The CPRD cohort included 2 475 360 patients aged 18-100 years with over 3.8 million person-years of follow-up. 234 204 of these patients had one or more emergency admissions during follow-up. We excluded patients without a valid NHS number and a valid Townsend score.EndpointFirst (ie, incident) emergency admission to hospital in the next 2 years as recorded on the linked hospital episodes records.Risk factorsCandidate variables recorded on the general practitioner computer system including (1) demographic variables (age, sex, strategic health authority, Townsend deprivation score, ethnicity); (2) lifestyle variables (smoking, alcohol intake); (3) chronic diseases; (4) prescribed medication; (5) clinical values (body mass index, systolic blood pressure); (6) laboratory test results (haemoglobin, platelets, erythrocyte sedimentation rate, ratio of total serum cholesterol to high density lipoprotein cholesterol concentrations, liver function tests). We also included the number of emergency admissions in the preceding year based on information recorded on the linked hospital episodes records.ResultsThe final QAdmissions algorithm incorporated 30 variables. When applied to the QResearch validation cohort, it explained 41% of the variation in women and 43% of that in men. The D statistic for QAdmissions was 1.7 in women and 1.8 in men. The receiver operating curve statistic was 0.78 for men and 0.77 for women. QAdmissions had good performance on all measures of discrimination and calibration. The positive predictive value for emergency admissions for the top tenth of patients at highest risk was 42% and the sensitivity was 39%. The results for the CPRD validation cohort were similar.ConclusionsThe QAdmissions model provided a valid measure of absolute risk of emergency admission to hospital in the general population as shown by its performance in a separate validation cohort. Further research is needed to evaluate the cost-effectiveness of using these algorithms in primary care.

Project description:BackgroundAcute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model-Accurate Prediction of Prolonged Ventilation (APPROVE)-to identify patients at risk of death or respiratory failure requiring >= 48 h of MV.MethodsThis was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort.ResultsThere were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85-0.88) in 2013 and 0.90 (95% CI 0.84-0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%).ConclusionsAn automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification.Trial registrationClinicalTrials.gov, NCT02488174 . Registered on 18 March 2015.

Project description:Accurate estimation of favorable neurological survival after in-hospital cardiac arrest could provide critical information for physicians, patients, and families.Within the Get With the Guidelines-Resuscitation registry, we identified 42,957 patients from 551 hospitals admitted between January 2000 and October 2009 who were successfully resuscitated from an in-hospital cardiac arrest. A simple prediction tool for favorable neurological survival in patients successfully resuscitated from an in-hospital cardiac arrest was developed using multivariate logistic regression, with two-thirds of the sample randomly selected as the derivation cohort and one-third as the validation cohort. Favorable neurological status was defined as the absence of severe neurological deficits (cerebral performance category score of ?2).Rates of favorable neurological survival were similar in the derivation cohort (7052 patients [24.6%]) and validation cohort (3510 patients [24.5%]). Eleven variables were associated with favorable neurological survival: younger age, initial cardiac arrest rhythm of ventricular fibrillation or pulseless ventricular tachycardia with a defibrillation time of 2 minutes or less, baseline neurological status without disability, arrest location in a monitored unit, shorter duration of resuscitation, and absence of mechanical ventilation, renal insufficiency, hepatic insufficiency, sepsis, malignant disease, and hypotension prior to the arrest. The model had excellent discrimination (C statistic of 0.80 for both the derivation and validation cohorts) and calibration. The prediction tool demonstrated the ability to identify patients across a wide range of rates of favorable neurological survival: patients in the top decile had a 70.7% probability of this outcome, whereas patients in the bottom decile had a 2.8% probability.Among successfully resuscitated patients with an in-hospital cardiac arrest, a simple, bedside prediction tool provides robust estimates of the probability of favorable neurological survival. This tool permits accurate prognostication after cardiac arrest for physicians, patients, and families.

Dataset Information

Predicting in-hospital mortality for initial survivors of acute respiratory compromise (ARC) events: Development and validation of the ARC Score.

Publications

Predicting in-hospital mortality for initial survivors of acute respiratory compromise (ARC) events: Development and validation of the ARC Score.

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