Carl van Walraven
Biographic Data
| ID | 5408220 |
|---|---|
| NAME | Carl van Walraven |
| GIVEN NAMES | Carl |
| FAMILY NAME | van Walraven |
| SIGNATURE | VAN WALRAVEN C |
| AFFILIATIONS | Ottawa Hospital |
| ORCID | 0000-0002-8390-0930 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2001 |
| LATEST PUBLICATION YEAR | 2014 |
| H-INDEX | 1 |
Elective, Major Noncardiac Surgery on the Weekend
IMPORTANCE: Previous research has demonstrated that patients undergoing elective surgery on the weekend had an adjusted risk of 30-day mortality that was significantly higher than that of patients operated upon during the week. The generalizability of this association and effect size is unknown. OBJECTIVES: The aim of this study was to investigate the generalizability of the association between elective weekend surgery and increased 30-day postop…
The Mortality Risk Score and the ADG Score
BACKGROUND: Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults. OBJECTIVES: To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults…
Derivation and Validation of a Model to Predict Daily Risk of Death in Hospital
BACKGROUND: As electronic patient data from automated hospital databases become increasingly available, it is important to explore the ways in which these data could be used for the purposes other than patient care, such as quality assurance and improvement. OBJECTIVE: To determine if information from automated patient databases can be used to derive a model that can predict patients' daily risk of death in hospital. Such a model could be used to…
Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to Predict Mortality in a General Adult Population Cohort in Ontario, Canada
BACKGROUND: Administrative healthcare databases are increasingly used for health services and comparative effectiveness research. When comparing outcomes between different treatments, interventions, or exposures, the ability to adjust for differences in the risk of the outcome occurring between treatment groups is important. Similarly, when conducting healthcare provider profiling, adequate risk-adjustment is necessary for conclusions about provi…
A Modification of the Elixhauser Comorbidity Measures Into a Point System for Hospital Death Using Administrative Data
BACKGROUND: Comorbidity measures are necessary to describe patient populations and adjust for confounding. In direct comparisons, studies have found the Elixhauser comorbidity system to be statistically slightly superior to the Charlson comorbidity system at adjusting for comorbidity. However, the Elixhauser classification system requires 30 binary variables, making its use for reporting and analysis of comorbidity cumbersome. OBJECTIVE: Modify t…
An Introduction to Multilevel Regression Models
An Introduction to Multilevel Regression Models
A Modification of the Elixhauser Comorbidity Measures Into a Point System for Hospital Death Using Administrative Data
BACKGROUND: Comorbidity measures are necessary to describe patient populations and adjust for confounding. In direct comparisons, studies have found the Elixhauser comorbidity system to be statistically slightly superior to the Charlson comorbidity system at adjusting for comorbidity. However, the Elixhauser classification system requires 30 binary variables, making its use for reporting and analysis of comorbidity cumbersome. OBJECTIVE: Modify t…
The Mortality Risk Score and the ADG Score
BACKGROUND: Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults. OBJECTIVES: To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults…
Derivation and Validation of a Model to Predict Daily Risk of Death in Hospital
BACKGROUND: As electronic patient data from automated hospital databases become increasingly available, it is important to explore the ways in which these data could be used for the purposes other than patient care, such as quality assurance and improvement. OBJECTIVE: To determine if information from automated patient databases can be used to derive a model that can predict patients' daily risk of death in hospital. Such a model could be used to…
Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to Predict Mortality in a General Adult Population Cohort in Ontario, Canada
BACKGROUND: Administrative healthcare databases are increasingly used for health services and comparative effectiveness research. When comparing outcomes between different treatments, interventions, or exposures, the ability to adjust for differences in the risk of the outcome occurring between treatment groups is important. Similarly, when conducting healthcare provider profiling, adequate risk-adjustment is necessary for conclusions about provi…
Elective, Major Noncardiac Surgery on the Weekend
IMPORTANCE: Previous research has demonstrated that patients undergoing elective surgery on the weekend had an adjusted risk of 30-day mortality that was significantly higher than that of patients operated upon during the week. The generalizability of this association and effect size is unknown. OBJECTIVES: The aim of this study was to investigate the generalizability of the association between elective weekend surgery and increased 30-day postop…
Internal Medicine (5 works) · Internal Medicine (5 works) · Medicine (5 works) · Emergency Medicine (4 works) · Emergency Medicine (4 works) · Logistic regression (4 works) · Retrospective cohort study (4 works) · Statistics (4 works) · Chronic Disease Management Strategies (3 works) · Cohort study (3 works)