Ability of prehospital NEWS to predict 1-day and 7-day mortality is reduced in the older adult patients

2021 ◽  
pp. emermed-2019-209400
Author(s):  
Jussi Pirneskoski ◽  
Mitja Lääperi ◽  
Markku Kuisma ◽  
Klaus T Olkkola ◽  
Jouni Nurmi
Keyword(s):  
Age Groups ◽  
Predictive Performance ◽  
Mortality Prediction ◽  
Early Warning Score ◽  
Additional Parameter ◽  
Short Term ◽  
Net Reclassification Improvement ◽  
Significant Independent Risk Factor ◽  
Record Data ◽  
Short Term Mortality

BackgroundNational Early Warning Score (NEWS) does not include age as a parameter despite age is a significant independent risk factor of death. The aim of this study was to examine whether age has an effect on predictive performance of short-term mortality of NEWS in a prehospital setting. We also evaluated whether adding age as an additional parameter to NEWS improved its short-term mortality prediction.MethodsWe calculated NEWS scores from retrospective prehospital electronic patient record data for patients 18 years or older with sufficient prehospital data to calculate NEWS. We used area under receiver operating characteristic (AUROC) to analyse the predictive performance of NEWS for 1 and 7 day mortalities with increasing age in three different age groups: <65 years, 65–79 years and ≥80 years. We also explored the ORs for mortality of different NEWS parameters in these age groups. We added age to NEWS as an additional parameter and evaluated its effect on predictive performance.ResultsWe analysed data from 35 800 ambulance calls. Predictive performance for 7-day mortality of NEWS decreased with increasing age: AUROC (95% CI) for 1-day mortality was 0.876 (0.848 to 0.904), 0.824 (0.794 to 0.854) and 0.820 (0.788 to 0.852) for first, second and third age groups, respectively. AUROC for 7-day mortality had a similar trend. Addition of age as an additional parameter to NEWS improved its ability to predict short-term mortality when assessed with continuous Net Reclassification Improvement.ConclusionsAge should be considered as an additional parameter to NEWS, as it improved its performance in predicting short-term mortality in this prehospital cohort.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights. Conclusions All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

scholarly journals A machine learning approach to predicting short-term mortality risk for patients starting chemotherapy.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6538-6538
Author(s):  
Ravi Bharat Parikh ◽  
Aymen Elfiky ◽  
Maximilian J. Pany ◽  
Ziad Obermeyer
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Cancer Center ◽  
Short Term ◽  
Model Predictions ◽  
Short Term Mortality ◽  
Chemotherapy Initiation ◽  
Predicted Risk

6538 Background: Patients who die soon after starting chemotherapy incur symptoms and financial costs without survival benefit. Prognostic uncertainty may contribute to increasing chemotherapy use near the end of life, but few prognostic aids exist to guide physicians and patients in the decision to initiate chemotherapy. Methods: We obtained all electronic health record (EHR) data from 2004-14 from a large national cancer center, linked to Social Security data to determine date of death. Using EHR data before treatment initiation, we created a machine learning (ML) model to predict 180-day mortality from the start of chemotherapy. We derived the model using data from 2004-11 and report predictive performance on data from 2012-14. Results: 26,946 patients initiated 51,774 discrete chemotherapy regimens over the study period; 49% received multiple lines of chemotherapy. The most common cancers were breast (23.6%), colorectal (17.6%), and lung (16.6%). 18.4% of patients died within 180 days after chemotherapy initiation. Model predictions were used to rank patients in the validation cohort by predicted risk. Patients in the highest decile of predicted risk had a 180-day mortality of 74.8%, vs. 0.2% in the lowest decile (area under the receiver-operating characteristic curve [AUC] 0.87). Predictions were accurate for patients with metastatic disease (AUC 0.85) and for individual primary cancers and chemotherapy regimens—including experimental regimens not present in the derivation sample. Model predictions were valid for 30- and 90-day mortality (AUC 0.94 and 0.89, respectively). ML predictions outperformed regimen-based mortality estimates from randomized trials (RT) (AUC 0.77 [ML] vs. 0.56 [RT]), and National Cancer Institute Surveillance, Epidemiology, and End Results Program (SEER) estimates (AUC 0.81 [ML] vs. 0.40 [SEER]). Conclusions: Using EHR data from a single cancer center, we derived a machine learning algorithm that accurately predicted short-term mortality after chemotherapy initiation. Further research is necessary to determine applications of this algorithm in clinical settings and whether this tool can improve shared decision making leading up to chemotherapy initiation.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2020 ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Generalized Additive Models ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
Net Reclassification Improvement ◽  
C Statistic ◽  
Patient Health Status ◽  
Patient Health

Abstract Background When chronic conditions are associated with outcomes such as mortality, comorbidity measures are essential both to describe patient health status and to adjust for potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, as optimal comorbidity weightings remain undetermined. The present study aimed to derive a set of new population-based Elixhauser comorbidity weightings, then to validate and compare their mortality predictivity against those of the Charlson and Elixhauser-based van Walraven weightings estimates in a population-based cohort.Methods Retrospective analysis was conducted with routine Swiss general hospital (102 hospitals) data (2012–2017) for 6.09 million inpatient cases. To derive the population-based weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results for Part 1 alongside the established weighting systems used for Part 2. Charlson and van Walraven weightings were applied to Charlson and Elixhauser comorbidity indices. Generalized additive models were weighted and adjusted for age, gender and hospital types.Results Overall, the population-based weights’ c-statistic (0.867, 95% CI: 0.865–0.868) was consistently higher than Elixhauser-van Walraven’s (0.863, 95% CI: 0.862–0.864) and Charlson’s (0.850, 95% CI: 0.849–0.851) in the derivation and validation groups and net reclassification improvement of new weights offers improved predictive performance of 0.4% on the Elixhauser-van Walraven and 6.1% on the Charlson weightings.Conclusions All weightings were validated with the national dataset and the new population-based weightings model improved the prediction of in-hospital mortality. The newly derive weights support patient population-based analysis of health outcomes.

scholarly journals Predictive Value of 3 Clinical Criteria for Sepsis (Quick Sequential Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and National Early Warning Score) With Respect to Short-term Mortality in Allogeneic Hematopoietic Cell Transplant Recipients With Suspected Infections

2020 ◽  
Author(s):  
Margaret L Lind ◽  
Amanda I Phipps ◽  
Stephen Mooney ◽  
Catherine Liu ◽  
Alison Fohner ◽  
...  
Keyword(s):  
Systemic Inflammatory Response Syndrome ◽  
Early Warning Score ◽  
Cell Transplant ◽  
Short Term ◽  
Hematopoietic Cell Transplant ◽  
Clinical Criteria ◽  
Allogeneic Hematopoietic Cell Transplant ◽  
Failure Assessment ◽  
Short Term Mortality ◽  
National Early Warning Score

Abstract Background Sepsis disproportionately affects allogeneic hematopoietic cell transplant (HCT) recipients and is challenging to define. Clinical criteria that predict mortality and intensive care unit end-points in patients with suspected infections (SIs) are used in sepsis definitions, but their predictive value among immunocompromised populations is largely unknown. Here, we evaluate 3 criteria among allogeneic HCT recipients with SIs. Methods We evaluated Systemic Inflammatory Response Syndrome (SIRS), quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS) in relation to short-term mortality among recipients transplanted between September 2010 and July 2017. We used cut-points of ≥ 2 for qSOFA/SIRS and ≥ 7 for NEWS and restricted to first SI per hospital encounter during patients’ first 100 days posttransplant. Results Of the 880 recipients who experienced ≥ 1 SI, 58 (6.6%) died within 28 days and 22 (2.5%) within 10 days of an SI. In relation to 10-day mortality, SIRS was the most sensitive (91.3% [95% confidence interval {CI}, 72.0%–98.9%]) but least specific (35.0% [95% CI, 32.6%–37.5%]), whereas qSOFA was the most specific (90.5% [95% CI, 88.9%–91.9%]) but least sensitive (47.8% [95% CI, 26.8%–69.4%]). NEWS was moderately sensitive (78.3% [95% CI, 56.3%–92.5%]) and specific (70.2% [95% CI, 67.8%–72.4%]). Conclusions NEWS outperformed qSOFA and SIRS, but each criterion had low to moderate predictive accuracy, and the magnitude of the known limitations of qSOFA and SIRS was at least as large as in the general population. Our data suggest that population-specific criteria are needed for immunocompromised patients.

scholarly journals 891. Epidemiology and Outcomes of Sepsis in Previously Healthy Patients

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S22-S23
Author(s):  
Mohammad Alrawashdeh ◽  
Michael Klompas ◽  
Steven Q Simpson ◽  
Sameer S Kadri ◽  
Russell Poland ◽  
...  
Keyword(s):  
Healthy People ◽  
Hospital Death ◽  
Short Term ◽  
Electronic Health Record Data ◽  
High Profile ◽  
Increased Risk ◽  
Record Data ◽  
Short Term Mortality ◽  
Sepsis Detection ◽  
Community Onset

Abstract Background Devastating cases of sepsis in previously healthy patients have received widespread attention and helped catalyze state and national mandates to improve sepsis detection and care. It is unclear, however, what proportion of sepsis cases occur in previously healthy people and how their outcomes compare to patients with comorbidities. Methods We conducted a retrospective study of adults admitted from 2009 to 2015 to 373 US hospitals from 3 cohorts using detailed electronic health record data. We identified patients with community-onset sepsis using CDC Adult Sepsis Event criteria and reviewed patients’ ICD-9-CM codes to identify major and minor comorbidities. Generalized linear mixed models were used to identify the association between healthy vs. comorbid status and short-term mortality (in-hospital death or discharge to hospice) among sepsis patients, controlling for demographics and clinical characteristics. Results The cohort included 6,715,286 adult hospitalizations, of which 337,983 (5%) met community-onset sepsis criteria. Most (329,052; 97.4%) sepsis patients had at least one comorbidity (96.1% major, 1.2% minor, 0.1% pregnant) whereas the minority (8,931; 2.6%) were previously healthy. Hospitalized patients without sepsis, by contrast, tended to be healthier (6.2%, Figure 1). Compared with sepsis patients with comorbidities, previously healthy sepsis patients were younger (mean 48.3 + 20 vs. 66.9 + 16.5 years, P < 0.001) and less likely to require ICU care on admission (30.9% vs. 50.5%, P < 0.001). Previously healthy patients were more likely to be discharged home vs. subacute facilities compared with sepsis patients with comorbidities but had higher short-term mortality rates (22.7% vs. 20.8%, P < 0.001) (Figure 2). The increased risk of short-term death in healthy patients persisted on multivariate analysis (adjusted odds ratios 1.36–1.79, P < 0.001). Conclusion The vast majority of patients who develop community-onset sepsis have pre-existing conditions. However, previously healthy patients may be at higher risk for death due to sepsis compared with patients with comorbidities. These findings provide context for high-profile reports about sepsis deaths in previously healthy people and underscore the importance of early sepsis recognition and treatment for all patients. Disclosures All Authors: No reported Disclosures.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2020 ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background: Understanding how comorbidity measures contribute to patient mortality are essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new population-based Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weightings in an adult in-patient population-based cohort of general hospitals. Methods: Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the population-based weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weightings were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results: Overall, the population-based weights’ c-statistic (0.867, 95% CI: 0.865–0.868) was consistently, yet minimally higher than Elixhauser-van Walraven’s (0.863, 95% CI: 0.862-0.864) and Charlson’s (0.850, 95% CI: 0.849–0.851) in the derivation and validation groups. The net reclassification improvement of new weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weightings.Conclusions: All weightings confirmed previous results with the national dataset. The new population-based weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

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