Dataset Information

Validation and comparison of two stroke prognostic models for in hospital, 30-day and 90-day mortality.

ABSTRACT:

Introduction

We aimed to validate and compare two clinical prognostic models for mortality which include the National Institutes of Health Stroke Scale (NIHSS); the Age and NIHSS Score (ANS) and case mix model (CMM) of the Sentinel Stroke National Audit Program (SSNAP). The NIHSS on admission was also tested as a prognostic score.

Patients and methods

Prospectively collected data from the SSNAP register for a cohort of patients (ischaemic and haemorrhagic stroke) admitted over 1 year to Gloucestershire Royal Hospital, England were accessed. The ANS and CMM were calculated and tested for in hospital, 30-day and 90-day mortality using calibration plots with Hosmer-Lemeshow tests, receiver operating characteristics curves and other measures of prognostic accuracy.

Results

Of 848 patients, 110 (12.9%) died in hospital, 112 (13.2%) at 30 days and 164 (19.2%) at 90 days. Calibration for all three scores was good, although Hosmer-Lemeshow test p values were <0.05 with the NIHSS alone for in hospital and 30-day deaths, suggesting deviation from good fit. The c-statistics for in hospital, 30-day and 90-day mortality were ANS (0.783, 0.782, 0.779) and CMM (0.783, 0.774, 0.758), respectively. The NIHSS alone showed fair discrimination but performed less well. A NIHSS score ?6 was associated with significant mortality (p?ConclusionA simple prognostic model containing age and admission NIHSS only, performed as well as a more complex score at predicting in hospital, 30-day and 90-day mortality. Admission NIHSS recording should be encouraged for stroke registries.

SUBMITTER: Dutta D

PROVIDER: S-EPMC6453188 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Publications

Validation and comparison of two stroke prognostic models for in hospital, 30-day and 90-day mortality.

Dutta Dipankar D Cannon Abigail A Bowen Emily E

European stroke journal 20170330 4

<h4>Introduction</h4>We aimed to validate and compare two clinical prognostic models for mortality which include the National Institutes of Health Stroke Scale (NIHSS); the Age and NIHSS Score (ANS) and case mix model (CMM) of the Sentinel Stroke National Audit Program (SSNAP). The NIHSS on admission was also tested as a prognostic score.<h4>Patients and methods</h4>Prospectively collected data from the SSNAP register for a cohort of patients (ischaemic and haemorrhagic stroke) admitted over 1 y ...[more]

PMID: 31008324

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Project description:BackgroundHospital-acquired pneumonia (HAP) is the most common hospital-acquired infection in China with substantial morbidity and mortality. But no specific risk assessment model has been well validated in patients with HAP. The aim of this study was to investigate the published risk assessment models that could potentially be used to predict 30-day mortality in HAP patients in non-surgical departments.MethodsThis study was a single-center, retrospective study. In total, 223 patients diagnosed with HAP from 2012 to 2017 were included in this study. Clinical and laboratory data during the initial 24 hours after HAP diagnosis were collected to calculate the pneumonia severity index (PSI); consciousness, urea nitrogen, respiratory rate, blood pressure, and age ≥65 years (CURB-65); Acute Physiology and Chronic Health Evaluation II (APACHE II); Sequential Organ Failure Assessment (SOFA); and Quick Sequential Organ Failure Assessment (qSOFA) scores. The discriminatory power was tested by constructing receiver operating characteristic (ROC) curves, and the areas under the curve (AUCs) were calculated.ResultsThe all-cause 30-day mortality rate was 18.4% (41/223). The PSI, CURB-65, SOFA, APACHE II, and qSOFA scores were significantly higher in non-survivors than in survivors (all P < 0.001). The discriminatory abilities of the APACHE II and SOFA scores were better than those of the CURB-65 and qSOFA scores (ROC AUC: APACHE II vs. CURB-65, 0.863 vs. 0.744, Z = 3.055, P = 0.002; APACHE II vs. qSOFA, 0.863 vs. 0.767, Z = 3.017, P = 0.003; SOFA vs. CURB-65, 0.856 vs. 0.744, Z = 2.589, P = 0.010; SOFA vs. qSOFA, 0.856 vs. 0.767, Z = 2.170, P = 0.030). The cut-off values we defined for the SOFA, APACHE II, and qSOFA scores were 4, 14, and 1.ConclusionsThese results suggest that the APACHE II and SOFA scores determined during the initial 24 h after HAP diagnosis may be useful for the prediction of 30-day mortality in HAP patients in non-surgical departments. The qSOFA score may be a simple tool that can be used to quickly identify severe infections.

Project description:BackgroundThe Centers for Medicare and Medicaid Services (CMS) penalizes hospitals for higher than expected 30-day mortality rates using methods without accounting for condition severity risk adjustment. For patients with stroke, CMS claims did not quantify stroke severity until recently, when the National Institutes of Health Stroke Scale (NIHSS) reporting began.ObjectiveExamine the predictive ability of claim-based NIHSS to predict 30-day mortality and 30-day hospital readmission in patients with ischemic stroke.DesignRetrospective cohort study of Medicare claims data.PatientsMedicare beneficiaries with ischemic stroke (N=43,241) acute hospitalization between October 2016 and November 2017.MeasurementsAll-cause 30-day mortality and 30-day hospital readmission. NIHSS score was derived from ICD-10 codes and stratified into the following: minor to moderate, moderate, moderate to severe, and severe categories.ResultsAmong 43,241 patients with ischemic stroke with NIHSS from 2,659 US hospitals, 64.6% had minor to moderate stroke, 14.3% had moderate, 12.7% had moderate to severe, and 8.5% had a severe stroke,10.1% died within 30 days, 12.1% were readmitted within 30 days. The NIHSS exhibited stronger discriminant property (C-statistic 0.83, 95% CI: 0.82-0.84) for 30-day mortality compared to Elixhauser (0.74, 95% CI: 0.73-0.75). A monotonic increase in the adjusted 30-day mortality risk occurred relative to minor to moderate stroke category: hazard ratio [HR]=2.92 (95% CI=2.59-3.29) for moderate stroke, HR=5.49 (95% CI=4.90-6.15) for moderate to severe stroke, and HR=7.82 (95% CI=6.95-8.80) for severe stroke. After accounting for competing risk of mortality, there was a significantly higher readmission risk in the moderate stroke (HR=1.11, 95% CI=1.03-1.20), but significantly lower readmission risk in the severe stroke (HR=0.84, 95% CI=0.74-0.95) categories.LimitationTiming of NIHSS reporting during hospitalization is unknown.ConclusionsMedicare claim-based NIHSS is significantly associated with 30-day mortality in Medicare patients with ischemic stroke and significantly improves discriminant property relative to the Elixhauser comorbidity index.

Project description:ImportanceQuestions have recently arisen as to whether 30-day mortality is a reasonable metric for understanding institutional practice differences after transcatheter aortic valve replacement (TAVR) and surgical aortic valve replacement (SAVR).ObjectiveTo examine the utility of 30-day vs 90-day mortality after TAVR and SAVR as a mortality quality metric.Design, setting, and participantsThis nationally representative, multicenter, cohort study analyzed data from Medicare beneficiaries undergoing TAVR and SAVR procedures from January 1, 2012, to December 31, 2015. Concomitant coronary artery bypass grafting and other heart valve or other major open-heart procedures were excluded. Hospitals that performed fewer than 50 TAVR or 70 SAVR procedures per year were excluded to ensure reliable estimates and to reduce the risks of inflated results because of small institutional sample sizes. Data were analyzed from October 2018 to August 2019.ExposuresHospitals were ranked into top- (10%), middle- (80%), and bottom-performing (10%) groups based on their 4-year mean 30-day mortality.Main outcomes and measuresChanges in hospital performance rankings at 90 days and 1 year and correlation of 30- and 90-day mortality with 1-year mortality were examined.ResultsA total of 30 329 TAVR admissions at 184 hospitals and 26 021 SAVR admissions at 191 hospitals were evaluated. For TAVR, 40 hospitals (21.7%) changed performance rankings at 90 days: 13 (48.1%) in the top-performing group and 8 (29.6%) in the bottom-performing group. At 1 year, 56 hospitals (30.4%), which included 21 (77.8%) in the top-performing group and 12 (44.4%) in the bottom-performing group, changed rankings. Similar findings were observed for SAVR, with an overall 90-day conversion rate of 17.3% and a 1-year rate of 30.3%. These findings persisted after adjusting for the differences in patient risk profiles among the 3 groups. Capturing 90-day events was also more robustly informative regarding expected 1-year outcomes after both TAVR and SAVR, largely owing to the observed plateau in the instantaneous hazard observed beyond this point.Conclusions and relevanceThe findings suggest that evaluation of hospital performance based on 30-day mortality may underestimate outcomes and therefore substantially misrepresent institutional performance after TAVR and SAVR compared with 90-day mortality, even after risk adjustment. Although 30-day mortality has been validated, 90-day mortality may be a more reliable outcome metric for measuring hospital performance and capturing procedure-related mortality.

Project description:The association between hospital volume and the death rate for patients who are hospitalized for acute myocardial infarction, heart failure, or pneumonia remains unclear. It is also not known whether a volume threshold for such an association exists.We conducted cross-sectional analyses of data from Medicare administrative claims for all fee-for-service beneficiaries who were hospitalized between 2004 and 2006 in acute care hospitals in the United States for acute myocardial infarction, heart failure, or pneumonia. Using hierarchical logistic-regression models for each condition, we estimated the change in the odds of death within 30 days associated with an increase of 100 patients in the annual hospital volume. Analyses were adjusted for patients' risk factors and hospital characteristics. Bootstrapping procedures were used to estimate 95% confidence intervals to identify the condition-specific volume thresholds above which an increased volume was not associated with reduced mortality.There were 734,972 hospitalizations for acute myocardial infarction in 4128 hospitals, 1,324,287 for heart failure in 4679 hospitals, and 1,418,252 for pneumonia in 4673 hospitals. An increased hospital volume was associated with reduced 30-day mortality for all conditions (P<0.001 for all comparisons). For each condition, the association between volume and outcome was attenuated as the hospital's volume increased. For acute myocardial infarction, once the annual volume reached 610 patients (95% confidence interval [CI], 539 to 679), an increase in the hospital volume by 100 patients was no longer significantly associated with reduced odds of death. The volume threshold was 500 patients (95% CI, 433 to 566) for heart failure and 210 patients (95% CI, 142 to 284) for pneumonia.Admission to higher-volume hospitals was associated with a reduction in mortality for acute myocardial infarction, heart failure, and pneumonia, although there was a volume threshold above which an increased condition-specific hospital volume was no longer significantly associated with reduced mortality.

Project description:BackgroundPredicting the risk of in-hospital mortality on admission is challenging but essential for risk stratification of patient outcomes and designing an appropriate plan-of-care, especially among transferred patients.ObjectiveDevelop a model that uses administrative and clinical data within 24 h of transfer to predict 30-day in-hospital mortality at an Academic Health Center (AHC).DesignRetrospective cohort study. We used 30 putative variables in a multiple logistic regression model in the full data set (n = 10,389) to identify 20 candidate variables obtained from the electronic medical record (EMR) within 24 h of admission that were associated with 30-day in-hospital mortality (p < 0.05). These 20 variables were tested using multiple logistic regression and area under the curve (AUC)-receiver operating characteristics (ROC) analysis to identify an optimal risk threshold score in a randomly split derivation sample (n = 5194) which was then examined in the validation sample (n = 5195).ParticipantsTen thousand three hundred eighty-nine patients greater than 18 years transferred to the Indiana University (IU)-Adult Academic Health Center (AHC) between 1/1/2016 and 12/31/2017.Main measuresSensitivity, specificity, positive predictive value, C-statistic, and risk threshold score of the model.Key resultsThe final model was strongly discriminative (C-statistic = 0.90) and had a good fit (Hosmer-Lemeshow goodness-of-fit test [X2 (8) =6.26, p = 0.62]). The positive predictive value for 30-day in-hospital death was 68%; AUC-ROC was 0.90 (95% confidence interval 0.89-0.92, p < 0.0001). We identified a risk threshold score of -2.19 that had a maximum sensitivity (79.87%) and specificity (85.24%) in the derivation and validation sample (sensitivity: 75.00%, specificity: 85.71%). In the validation sample, 34.40% (354/1029) of the patients above this threshold died compared to only 2.83% (118/4166) deaths below this threshold.ConclusionThis model can use EMR and administrative data within 24 h of transfer to predict the risk of 30-day in-hospital mortality with reasonable accuracy among seriously ill transferred patients.

Dataset Information

Validation and comparison of two stroke prognostic models for in hospital, 30-day and 90-day mortality.

Introduction

Patients and methods

Results

Publications

Validation and comparison of two stroke prognostic models for in hospital, 30-day and 90-day mortality.

Similar Datasets

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