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

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.

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

BMC Health Services Research ◽

10.1186/s12913-020-05999-5 ◽

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.

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

10.21203/rs.3.rs-39719/v2 ◽

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

10.21203/rs.3.rs-39719/v3 ◽

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 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 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 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 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 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 weightings.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.

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

10.21203/rs.3.rs-39719/v1 ◽

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.

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Procalcitonin Clearance at 24, 48, 72, and 96 Hours and Mortality in Patients With Cancer and Sepsis: A Retrospective Cohort Study

Journal of Intensive Care Medicine ◽

10.1177/0885066619861588 ◽

2019 ◽

Vol 35 (11) ◽

pp. 1297-1301

Author(s):

Antonio Paulo Nassar ◽

Beatriz Nicolau Nassif ◽

Daniel Vitório Veiga dos Santos ◽

Pedro Caruso

Keyword(s):

Cohort Study ◽

Hospital Mortality ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Predictive Performance ◽

Mortality Prediction ◽

Sepsis Management ◽

Receiver Operating Characteristic Curves ◽

Time Points ◽

Patients With Cancer

Introduction: Previous studies have evaluated procalcitonin clearance (PCTc) as a marker of sepsis severity but at different time points and cutoffs. We aimed to assess the predictive performance of PCTc at different time points of sepsis management in patients with cancer. Methods: This retrospective cohort study included patients with cancer admitted to an intensive care unit between 2013 and 2016. We calculated PCTc at 24, 48, 72, and 96 hours after admission. Its predictive performance for hospital and 90-day mortality was analyzed with receiver operating characteristic curves and areas under the curves (AUCs). Sensitivity and specificity were calculated for different time points using different cutoffs. Results: We included 301 patients. Areas under the curves ranged from 0.62 for PCTc at 24 hours to 0.68 for PCTc at 72 and 96 hours for hospital mortality prediction, and from 0.61 for PCTc at 24 hours to 0.68 for PCTc at 72 hours for 90-day mortality prediction. For hospital mortality prediction, PCTc at 72 hours ≤80% showed the best sensitivity (96.0%; 95% confidence interval [CI]: 90.8%-98.7%), and PCTc at 96 hours ≤50% showed the best specificity (70.7%; 95% CI: 54.5%-83.9%). Conclusions: Procalcitonin clearance at 24, 48, 72, and 96 hours poorly predicted hospital and 90-day mortality. Therefore, daily PCT measurement should not be used to predict mortality for patients with cancer and sepsis.

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Prenatal Manganese Exposure and Long-Term Neuropsychological Development at 4 Years of Age in a Population-Based Birth Cohort

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17051665 ◽

2020 ◽

Vol 17 (5) ◽

pp. 1665

Author(s):

Ainara Andiarena ◽

Amaia Irizar ◽

Amaia Molinuevo ◽

Nerea Urbieta ◽

Izaro Babarro ◽

...

Keyword(s):

Motor Development ◽

Generalized Additive Models ◽

Epidemiological Studies ◽

Population Based ◽

Specific Pattern ◽

Additive Models ◽

Linear Regression Models ◽

Neuropsychological Development ◽

Manganese Exposure ◽

Stratified Analysis

Background: Manganese (Mn) is an essential micronutrient for humans, the diet being the main source of exposure. Some epidemiological studies describe a negative association between prenatal Mn and later neuropsychological development, but results are inconsistent. The aim of this study was to explore the association between prenatal Mn exposure and neuropsychological development assessed at 4 years of age. Methods: Study subjects were 304 mother-child pairs from the Gipuzkoa cohort of the INMA (Environment and Childhood) Project. Mn was measured in newborns’ hair. Children’s neuropsychological development was assessed at 4 years of age using the McCarthy Scales of Children’s Abilities. Multivariate linear regression models were built. Stratified analysis by sex was performed. Generalized additive models were used to assess the shape of the relation. Results: The median Mn concentration in newborns’ hair was 0.42 μg/g (95% CI = 0.38, 0.46). The association between Mn levels and the neuropsychological development was not statistically significant for the general cognitive scale (β [95% CI] = 0.36 [−5.23, 5.95]), motor scale (β [95% CI] = 1.9 [−3.74, 7.55]) or any of the other outcomes. No sex-specific pattern was found. The best shape describing the relationship was linear for all the scales. Conclusion: Our results suggest that prenatal Mn concentrations measured in newborns’ hair do not affect cognitive or motor development at 4 years of age in boys or in girls at the observed Mn levels.

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How Do Biological Characteristics of Primary Intracranial Tumors Affect Their Clinical Presentation in Children and Young Adults?

Journal of Child Neurology ◽

10.1177/0883073818767562 ◽

2018 ◽

Vol 33 (8) ◽

pp. 503-511

Author(s):

Thomas P. C. Chu ◽

Anjali Shah ◽

David Walker ◽

Michel P. Coleman

Keyword(s):

Primary Care ◽

Rate Ratio ◽

Malignant Tumors ◽

Generalized Additive Models ◽

Similar Rate ◽

Population Based ◽

Additive Models ◽

Benign Tumors ◽

Intracranial Tumors ◽

Aggressive Tumors

We demonstrated the pattern in presentation of primary intracranial tumors in a population-based cohort of patients aged 0-24 years identified from the National Cancer Registry for England, using linked medical records from primary care and hospitals. We used generalized additive models to estimate temporal changes in presentation rates. Borderline and malignant tumors presented at a similar rate in primary care (6.4 and 6.6 consultations per 100 patients each month) and in hospital (3.4 and 3.6). Benign tumors presented earlier but less frequently (rate = 4.4 and rate ratio = 0.75, 95% CI = 0.60-0.93, in primary care; rate = 2.6 and rate ratio = 0.83, 95% CI = 0.77-0.89, in hospital). Many tumors began presenting shortly before their diagnosis, but less aggressive tumors were likely to present earlier in primary care. Earlier detection of less aggressive tumors in primary care may reduce the risk of complications and morbidity among survivors.

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

Emergency Medicine Journal ◽

10.1136/emermed-2019-209400 ◽

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.

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Hotspots of childhood obesity in a large metropolitan area: does neighbourhood social and built environment play a part?

International Journal of Epidemiology ◽

10.1093/ije/dyz205 ◽

2019 ◽

Vol 49 (3) ◽

pp. 934-943 ◽

Cited By ~ 3

Author(s):

Ana Isabel Ribeiro ◽

Ana Cristina Santos ◽

Verónica M Vieira ◽

Henrique Barros

Keyword(s):

Childhood Obesity ◽

Built Environment ◽

Metropolitan Area ◽

Fast Food ◽

Generalized Additive Models ◽

Geographic Area ◽

Population Based ◽

Additive Models ◽

Individual Level ◽

Neighbourhood Characteristics

Abstract Background Effective place-based interventions for childhood obesity call for the recognition of the high-risk neighbourhoods and an understanding of the determinants present locally. However, such an approach is uncommon. In this study, we identified neighbourhoods with elevated prevalence of childhood obesity (‘hotspots’) in the Porto Metropolitan Area and investigated to what extent the socio-economic and built environment characteristics of the neighbourhoods explained such hotspots. Methods We used data on 5203 7-year-old children from a population-based birth cohort, Generation XXI. To identify hotspots, we estimated local obesity odds ratios (OR) and 95% confidence intervals (95%CI) using generalized additive models with a non-parametric smooth for location. Measures of the socio-economic and built environment were determined using a Geographic Information System. Associations between obesity and neighbourhood characteristics were expressed as OR and 95%CI after accounting for individual-level variables. Results At 7 years of age, 803 (15.4%) children were obese. The prevalence of obesity varied across neighbourhoods and two hotspots were identified, partially explained by individual-level variables. Adjustment for neighbourhood characteristics attenuated the ORs and further explained the geographic variation. This model revealed an association between neighbourhood socio-economic deprivation score and obesity (OR = 1.014, 95%CI 1.004–1.025), as well as with the presence of fast-food restaurants at a walkable distance from the residence (OR = 1.37, 1.06–1.77). Conclusions In our geographic area it was possible to identify neighbourhoods with elevated prevalence of childhood obesity and to suggest that targeting such high-priority neighbourhoods and their environmental characteristics may help reduce childhood obesity.

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Optimal Oxygen Saturation Targets in Patients with Sepsis- Associated Encephalopathy: A Cohort Study from the MIMIC-IV Database

10.21203/rs.3.rs-478223/v1 ◽

2021 ◽

Author(s):

Lina Zhao ◽

Yunying Wang ◽

Zengzheng Ge ◽

Huadong Zhu ◽

Yi Li

Keyword(s):

Cohort Study ◽

Intensive Care ◽

Hospital Mortality ◽

Oxygen Saturation ◽

Medical Information ◽

Generalized Additive Models ◽

Additive Models ◽

Optimal Range ◽

Oxygenation Status ◽

The Impact

Abstract Objectives: Patients with sepsis-associated encephalopathy (SAE) in the intensive care unit (ICU) are treated with supplemental oxygen. However, few studies have investigated the impact of oxygenation status on the patient with SAE, and the optimal oxygenation status target remains unclear. We aimed to investigate the relationship between optimal oxygenation status and patients with SAE.Methods: This study is a retrospective cohort study. Patients were diagnosed with sepsis3.0 at the first ICU admission between 2008 and 2019 from Medical Information Mart for Intensive Care IV (MIMIC IV). We use generalized additive models to estimate the optimal oxygen saturation targets in patients with SAE. Multivariate logistic analysis to further confirm it. Measurements and Main Results: A total of 6714 patients with SAE were included. The incidence of patients with SAE was 66.8%, and hospital mortality was 7.9%. SpO2≤92% was the independent risk factor of incidence in patients with SAE. The optimal range of SpO2 was 93%–97%, which can reduce the incidence of patients with SAE. The optimal range of SpO2 was 92%–96%, reducing the hospital mortality of patients with SAE.Conclusions: The optimal range of SpO2 was 93%–96% reduce the hospital mortality and incidence of patients with SAE. SAE patients need conservative oxygen therapy

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