AI Article Synopsis

  • The study aimed to create a model to predict the risk of mortality after lung surgery, focusing on preoperative risk factors and complications.
  • It analyzed data from the American College of Surgeons NSQIP database of patients who underwent elective lobectomies between 2005 and 2012, evaluating various risk factors and complications.
  • The results identified 7 significant predictors of mortality, with some factors like dyspnea and dysnatremia showing an independent link to death, emphasizing the complexity of factors influencing postoperative outcomes.

Article Abstract

Background: Our goal was to develop a predictive model that identifies how preoperative risk factors and perioperative complications lead to mortality after anatomic pulmonary resections.

Study Design: This was a retrospective cohort study. The American College of Surgeons NSQIP database was examined for all patients undergoing elective lobectomies for cancer from 2005 through 2012. Fifty-eight pre- and intraoperative risk factors and 13 complications were considered for their impact on perioperative mortality within 30 days of surgery. Multivariate logistic regression and a logistic regression model using least absolute shrinkage and selection operator (LASSO) selection methods were used to identify preoperative risk factors that were significant for predicting mortality, either through or independent of complications. Only factors that were significant under both the multivariate logistic regression and LASSO-selected models were considered to be validated for the final model.

Results: There were 6,435 lobectomies identified. After multivariate logistic regression modeling, 28 risk factors and 5 complications were found to be predictors for mortality. This was then tested against the LASSO method. There were 7 factors shared between the LASSO and multivariate logistic regressions that predicted mortality based on comorbidity: age (p = 0.007), male sex (p = 0.011), open lobectomy (p = 0.001), preoperative dyspnea at rest (p < 0.001), preoperative dyspnea on exertion (p = 0.003), preoperative dysnatremia (serum sodium <135 mEq/L or >145 mEq/L) (p = 0.011), and preoperative anemia (p = 0.002). Of these, 3 variables predicted mortality independent of any complications: dyspnea at rest, dyspnea on exertion, and dysnatremia.

Conclusions: The clinical factors that predict postoperative complications and mortality are multiple and not necessarily aligned. Efforts to improve quality after anatomic pulmonary resections should focus on mechanisms to address both types of adverse outcomes.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jamcollsurg.2016.02.020DOI Listing

Publication Analysis

Top Keywords

risk factors
20
multivariate logistic
16
logistic regression
16
factors
8
mortality
8
nsqip database
8
preoperative risk
8
anatomic pulmonary
8
factors complications
8
mortality independent
8

Similar Publications

In this study, we first analyzed data from 147 patients with solitary plasmacytomas treated at the Mayo Clinic between 2005 and 2022 and then expanded our investigation through a systematic review and meta-analysis of 62 studies, encompassing 3,487 patients from the years 1960 to 2022. Our findings reveal that patients with up to 10% clonal plasma cells in their bone marrow (BM), denoted as plasmacytoma +, had a significantly reduced median disease-free survival (DFS) of 15.7 months vs.

View Article and Find Full Text PDF

Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.

Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.

View Article and Find Full Text PDF

Background: Health worker migration from Nigeria poses significant challenges to the Nigerian health care sector and has far-reaching implications for health care systems globally. Understanding the factors driving migration, its effects on health care delivery, and potential policy interventions is critical for addressing this complex issue.

Objective: This study aims to comprehensively examine the factors encouraging the emigration of Nigerian health workers, map out the effects of health worker migration on the Nigerian health system, document the loss of investment in health training and education resulting from migration, identify relevant policy initiatives addressing migration, determine the effects of Nigerian health worker migration on destination countries, and identify the benefits and demerits to Nigeria of health worker migration.

View Article and Find Full Text PDF

Comparison of 3 Aging Metrics in Dual Declines to Capture All-Cause Dementia and Mortality Risk: Cohort Study.

JMIR Aging

January 2025

Department of Geriatrics, Guangdong Provincial Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, No. 106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, China, 0898-66571684.

Background: The utility of aging metrics that incorporate cognitive and physical function is not fully understood.

Objective: We aim to compare the predictive capacities of 3 distinct aging metrics-motoric cognitive risk syndrome (MCR), physio-cognitive decline syndrome (PCDS), and cognitive frailty (CF)-for incident dementia and all-cause mortality among community-dwelling older adults.

Methods: We used longitudinal data from waves 10-15 of the Health and Retirement Study.

View Article and Find Full Text PDF

Outcomes of Home Isolation Care Among COVID-19 Patients During the 2021 Epidemic Crisis in the Bangkok Metropolitan Region, Thailand.

Am J Public Health

January 2025

Teeraboon Lertwanichwattana and Ram Rangsin are with Phramongkutklao College of Medicine, Bangkok, Thailand. Supattra Srivanichakorn, Sairat Noknoy, and Sirinapa Siriporn Na Ratchaseema are with the Royal College of Family Physicians of Thailand, Bangkok. Nittaya Phanuphak is with the Institute of HIV Research and Innovation, Bangkok. Kitti Wongthavarawat is with the National Science and Technology Development Agency, Bangkok. Arunotai Siriussawakul, Varalak Srinonprasert, and Pattara Leelahavarong are with the Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok. Parawee Chevaisrakul and Putthapoom Lumjiaktase are with the Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok. Aree Kumpitak is with the Thai Network of People Living With HIV, Bangkok. Nopphan Phromsri is with the Human Settlement Foundation, Bangkok. Yupadee Sirisinsuk is with the Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok. Pongtorn Kietdumrongwong is with the Bangkok Dusit Medical Services, Bangkok. Apinun Aramrattana is with the Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.

To determine the overall mortality and risk factors of COVID-19 patients who were admitted to the Home Isolation (HI) program in Bangkok, Thailand, during the epidemic crisis in 2021. We conducted a retrospective cohort study using the data from a government telehealth application from July to December 2021. The vital status was verified from the government database on September 20, 2022.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!