Factors Affecting One-Year Outcomes After Major Lower Extremity Amputation in the Vascular Quality Initiative Amputation Registry

Introduction Major lower extremity amputation (LEA) results in significant morbidity and mortality. This study identifies factors contributing to adverse long-term outcomes after major LEA. Study Design Amputations in the Vascular Quality Initiative (VQI) long-term follow-up database from 2012 to 2017 were included. Multivariable logistic regression determined which significant patient factors were associated with 1-year mortality, long-term functional status, and progression to higher level amputation within 1 year. Results 3440 major LEAs were performed and a mortality rate of 19.9% was seen at 1 year. Logistic regression demonstrated that 1-year mortality was associated with post-op myocardial infarction (MI) (odds ratio (OR) 1.7, CI 1.02-2.97, P = .04), congestive heart failure (CHF) (OR 1.9, confidence interval (CI) 1.56-2.38, P < .001), hypertension (HTN) (OR 1.31, CI 1.00-1.72, P = .05), chronic obstructive pulmonary disease (COPD) (OR 1.36, CI 1.13-1.63, P < .001), and dependent functional status (OR 2.01, CI 1.67-2.41, P < .001). A decline in ambulatory status was associated with COPD (OR 1.36, CI 1.09-1.68, P = .006). Dependent functional status was protective against revision to higher level amputation (OR .18, CI .07-.45, P < .001). Conclusion In the VQI, 1-year mortality after major LEA is nearly 20% and associated with HTN, CHF, COPD, dependent functional status, and post-op MI. Decreased functional status at 1 year was associated with COPD, and progression to higher level amputation was less likely in patients with dependent functional status.

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The association between smoking behaviour patterns and chronic obstructive pulmonary disease: a long-term follow-up study among Finnish adults

The Clinical Respiratory Journal ◽

10.1111/j.1752-699x.2011.00266_5.x ◽

2011 ◽

Vol 5 ◽

pp. 6-7

Author(s):

Maria Hukkinen ◽

Tellervo Korhonen ◽

Kauko Heikkilä ◽

Jaakko Kaprio

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Smoking Behaviour ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Follow Up Study ◽

Long Term Follow Up

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Factors Affecting Length of Stay and Discharge Needs After Lower Extremity Amputation

Journal of Vascular Surgery ◽

10.1016/j.jvs.2016.06.066 ◽

2016 ◽

Vol 64 (3) ◽

pp. 854

Author(s):

Anna Z. Fashandi ◽

Lily E. Johnston ◽

Gilbert R. Upchurch ◽

J. Hunter Mehaffey ◽

William P. Robinson ◽

...

Keyword(s):

Length Of Stay ◽

Lower Extremity ◽

Lower Extremity Amputation ◽

Factors Affecting ◽

Extremity Amputation

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Is hypercapnia associated with poor prognosis in chronic obstructive pulmonary disease? A long-term follow-up cohort study

BMJ Open ◽

10.1136/bmjopen-2015-008909 ◽

2015 ◽

Vol 5 (12) ◽

pp. e008909 ◽

Cited By ~ 12

Author(s):

Hui Yang ◽

Pingchao Xiang ◽

Erming Zhang ◽

Weian Guo ◽

Yanwei Shi ◽

...

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Cohort Study ◽

Pulmonary Disease ◽

Poor Prognosis ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Long Term Follow Up

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Machine learning methods for diagnosis of chronic obstructive pulmonary disease in healthy subjects: Analysis of Risk factors To DEtect COPD (ARTDECO) (Preprint)

10.2196/preprints.24796 ◽

2020 ◽

Author(s):

Shigeo Muro ◽

Masato Ishida ◽

Yoshiharu Horie ◽

Wataru Takeuchi ◽

Shunki Nakagawa ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Chronic Obstructive Pulmonary Disease ◽

Logistic Regression ◽

Pulmonary Disease ◽

Vital Capacity ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Copd Diagnosis

BACKGROUND Airflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances including tobacco smoke is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist. OBJECTIVE To predict risk factors for COPD diagnosis using machine learning in an annual medical check-up database. METHODS In this retrospective, observational cohort study (Analysis of Risk factors To DEtect COPD [ARTDECO]), annual medical check-up records for all Hitachi Ltd. employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30–75 years, without prior diagnosis of COPD, asthma, or history of cancer were included. The database included clinical measurements (e.g., pulmonary function tests) and questionnaire responses. To predict risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning method (XGBoost) was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the pre-bronchodilator forced expiratory volume in 1 second (FEV1) to pre-bronchodilator forced vital capacity (FVC) was <0.7 during two consecutive examinations. RESULTS Of the 26,101 individuals screened, 1,213 met the exclusion criteria and thus 24,815 individuals were included in the analysis. The top 10 predictors for COPD diagnosis were FEV1/FVC, smoking status, allergic symptoms, cough, pack years, hemoglobin A1c, serum albumin, mean corpuscular volume, percent predicted vital capacity value, and percent predicted value of FEV1. The area under the receiver operating characteristic curves of the XGBoost model and the logistic regression model were 0.956 and 0.943, respectively. CONCLUSIONS Using a machine learning model in this longitudinal database, we identified a set multiple of parameters as risk factors other than smoking exposure or lung function to support general practitioners and occupational health physicians to predict the development of COPD. Further research to confirm our results is warranted, as our analysis involved a database used only in Japan. CLINICALTRIAL Not applicable.

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