Using Computer-based Medical Records to Predict Mortality Risk for Inner-city Patients with Reactive Airways Disease

William M. Tierney, Michael Murray, Denise L. Gaskins, Xiao Hua Zhou

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Abstract Objective: To use routine data from a comprehensive electronic medical record system to predict death among patients with reactive airways disease. Design: Retrospective cohort study conducted in an academic primary care internal medicine practice. Subjects were 1,536 adults with reactive airways disease: 542 with asthma and 994 with chronic obstructive pulmonary disease (COPD). Measurements: The dependent variable was death from any cause within 3 years following patients' first primary care appointment in 1992. Multivariable logistic regression was used to identify independent predictors of 3-year mortality, with half of the patients used to derive the predictive model and the other half used to assess its predictability. Results: Of the 1,536 study patients, 191 (12%) died in the 3-year follow-up period. From information available on or before patients' first primary care visit in 1992, multivariable predictors of 3-year mortality were coincidental heart failure, male sex, presence of COPD, lower weight, low serum albumin concentration level, and a prior arterial PO2 of less than 60 mmHg; use of an inhaled corticosteroid was protective. The c-statistic (ROC curve area) in the validation cohort was 0.76, indicating good discrimination, and goodness of fit was excellent by Hosmer-Lemeshow chi-square (P > 0.5). Only 24% of the patients in the validation cohort were designated at high risk (estimated ≥ 15% 3-year mortality), but this group contained more than half of the deaths within 3 years for the entire cohort. Conclusions: Data generated during routine care and stored in a comprehensive electronic medical record can accurately predict mortality among patients with reactive airways disease. Such technology can be used by practices to control for severity of illness when assessing clinical practice and to identify high-risk patients for interventions to improve prognosis.

Original languageEnglish
Pages (from-to)313-321
Number of pages9
JournalJournal of the American Medical Informatics Association
Volume4
Issue number4
StatePublished - Jul 1997

Fingerprint

Medical Records
Mortality
Primary Health Care
Electronic Health Records
Chronic Obstructive Pulmonary Disease
Internal Medicine
Serum Albumin
ROC Curve
Cause of Death
Appointments and Schedules
Adrenal Cortex Hormones
Cohort Studies
Asthma
Heart Failure
Retrospective Studies
Logistic Models
Technology
Weights and Measures

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Using Computer-based Medical Records to Predict Mortality Risk for Inner-city Patients with Reactive Airways Disease. / Tierney, William M.; Murray, Michael; Gaskins, Denise L.; Zhou, Xiao Hua.

In: Journal of the American Medical Informatics Association, Vol. 4, No. 4, 07.1997, p. 313-321.

Research output: Contribution to journalArticle

@article{ce45ccd6d6384a28ad02f5196441de72,
title = "Using Computer-based Medical Records to Predict Mortality Risk for Inner-city Patients with Reactive Airways Disease",
abstract = "Abstract Objective: To use routine data from a comprehensive electronic medical record system to predict death among patients with reactive airways disease. Design: Retrospective cohort study conducted in an academic primary care internal medicine practice. Subjects were 1,536 adults with reactive airways disease: 542 with asthma and 994 with chronic obstructive pulmonary disease (COPD). Measurements: The dependent variable was death from any cause within 3 years following patients' first primary care appointment in 1992. Multivariable logistic regression was used to identify independent predictors of 3-year mortality, with half of the patients used to derive the predictive model and the other half used to assess its predictability. Results: Of the 1,536 study patients, 191 (12{\%}) died in the 3-year follow-up period. From information available on or before patients' first primary care visit in 1992, multivariable predictors of 3-year mortality were coincidental heart failure, male sex, presence of COPD, lower weight, low serum albumin concentration level, and a prior arterial PO2 of less than 60 mmHg; use of an inhaled corticosteroid was protective. The c-statistic (ROC curve area) in the validation cohort was 0.76, indicating good discrimination, and goodness of fit was excellent by Hosmer-Lemeshow chi-square (P > 0.5). Only 24{\%} of the patients in the validation cohort were designated at high risk (estimated ≥ 15{\%} 3-year mortality), but this group contained more than half of the deaths within 3 years for the entire cohort. Conclusions: Data generated during routine care and stored in a comprehensive electronic medical record can accurately predict mortality among patients with reactive airways disease. Such technology can be used by practices to control for severity of illness when assessing clinical practice and to identify high-risk patients for interventions to improve prognosis.",
author = "Tierney, {William M.} and Michael Murray and Gaskins, {Denise L.} and Zhou, {Xiao Hua}",
year = "1997",
month = "7",
language = "English",
volume = "4",
pages = "313--321",
journal = "Journal of the American Medical Informatics Association : JAMIA",
issn = "1067-5027",
publisher = "Oxford University Press",
number = "4",

}

TY - JOUR

T1 - Using Computer-based Medical Records to Predict Mortality Risk for Inner-city Patients with Reactive Airways Disease

AU - Tierney, William M.

AU - Murray, Michael

AU - Gaskins, Denise L.

AU - Zhou, Xiao Hua

PY - 1997/7

Y1 - 1997/7

N2 - Abstract Objective: To use routine data from a comprehensive electronic medical record system to predict death among patients with reactive airways disease. Design: Retrospective cohort study conducted in an academic primary care internal medicine practice. Subjects were 1,536 adults with reactive airways disease: 542 with asthma and 994 with chronic obstructive pulmonary disease (COPD). Measurements: The dependent variable was death from any cause within 3 years following patients' first primary care appointment in 1992. Multivariable logistic regression was used to identify independent predictors of 3-year mortality, with half of the patients used to derive the predictive model and the other half used to assess its predictability. Results: Of the 1,536 study patients, 191 (12%) died in the 3-year follow-up period. From information available on or before patients' first primary care visit in 1992, multivariable predictors of 3-year mortality were coincidental heart failure, male sex, presence of COPD, lower weight, low serum albumin concentration level, and a prior arterial PO2 of less than 60 mmHg; use of an inhaled corticosteroid was protective. The c-statistic (ROC curve area) in the validation cohort was 0.76, indicating good discrimination, and goodness of fit was excellent by Hosmer-Lemeshow chi-square (P > 0.5). Only 24% of the patients in the validation cohort were designated at high risk (estimated ≥ 15% 3-year mortality), but this group contained more than half of the deaths within 3 years for the entire cohort. Conclusions: Data generated during routine care and stored in a comprehensive electronic medical record can accurately predict mortality among patients with reactive airways disease. Such technology can be used by practices to control for severity of illness when assessing clinical practice and to identify high-risk patients for interventions to improve prognosis.

AB - Abstract Objective: To use routine data from a comprehensive electronic medical record system to predict death among patients with reactive airways disease. Design: Retrospective cohort study conducted in an academic primary care internal medicine practice. Subjects were 1,536 adults with reactive airways disease: 542 with asthma and 994 with chronic obstructive pulmonary disease (COPD). Measurements: The dependent variable was death from any cause within 3 years following patients' first primary care appointment in 1992. Multivariable logistic regression was used to identify independent predictors of 3-year mortality, with half of the patients used to derive the predictive model and the other half used to assess its predictability. Results: Of the 1,536 study patients, 191 (12%) died in the 3-year follow-up period. From information available on or before patients' first primary care visit in 1992, multivariable predictors of 3-year mortality were coincidental heart failure, male sex, presence of COPD, lower weight, low serum albumin concentration level, and a prior arterial PO2 of less than 60 mmHg; use of an inhaled corticosteroid was protective. The c-statistic (ROC curve area) in the validation cohort was 0.76, indicating good discrimination, and goodness of fit was excellent by Hosmer-Lemeshow chi-square (P > 0.5). Only 24% of the patients in the validation cohort were designated at high risk (estimated ≥ 15% 3-year mortality), but this group contained more than half of the deaths within 3 years for the entire cohort. Conclusions: Data generated during routine care and stored in a comprehensive electronic medical record can accurately predict mortality among patients with reactive airways disease. Such technology can be used by practices to control for severity of illness when assessing clinical practice and to identify high-risk patients for interventions to improve prognosis.

UR - http://www.scopus.com/inward/record.url?scp=0031178659&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031178659&partnerID=8YFLogxK

M3 - Article

C2 - 9223037

AN - SCOPUS:0031178659

VL - 4

SP - 313

EP - 321

JO - Journal of the American Medical Informatics Association : JAMIA

JF - Journal of the American Medical Informatics Association : JAMIA

SN - 1067-5027

IS - 4

ER -