Pub Date : 2025-12-03DOI: 10.1001/jamasurg.2025.5282
John F. Jachimiak, Yasmin Arda, Carly C. Amon, Riley B. Brackin, Joshua S. Ng-Kamstra, John O. Hwabejire, Haytham M. A. Kaafarani, George C. Velmahos, Michael P. DeWane
Importance Unhoused individuals face significant structural barriers to postacute recovery following traumatic injury. However, national estimates of trauma readmission risk in this population remain limited. This study aimed to evaluate the association between unhoused status and 30-day hospital readmission after trauma. Objective To assess if unhoused status is linked to higher 30-day readmission rates after traumatic injury. Design, Setting, and Participants This retrospective cohort study used data from the 2017 through 2019 National Readmission Database. These data included a national, population-based sample of hospitalizations in the US. Participants included adults aged 18 years or older who were admitted for traumatic injury, identified using International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes. Elective admissions, in-hospital deaths, and discharges in December were excluded, given the 30-day outcome. Unhoused status was defined using ICD-10-CM code Z59.0. A total of 2 663 876 trauma admissions were included, of whom 46 381 were unhoused (1.7%). Patients were stratified by housing status based on the ICD-10 code for homelessness. The primary outcome was 30-day all-cause readmission. Multivariable logistic regression and marginal effects models estimated adjusted odds ratios (aOR) and predicted probabilities, controlling for demographic and clinical factors. Results Unhoused patients were substantially younger (65 years, 10.4% vs 59.4%), predominantly male (77.8% vs 48.2% female), and had much higher rates of substance use disorders (alcohol, 41.3% vs 9.9%; drug, 38.4% vs 5.1%) compared with housed patients; all comparisons were statistically significant ( P < .001). The 30-day readmission rate was significantly higher among unhoused patients (19.3% vs 12.2%; P < .001), with increased adjusted odds of readmission on multivariable analysis (aOR, 1.63; 95% CI, 1.58-1.67). Against medical advice discharge carried the highest readmission risk among unhoused patients (predicted probability, 30.3%; aOR, 1.81; 95% CI, 1.67-1.96). Unhoused patients were more likely to be readmitted for new traumatic injuries (aOR, 1.48; 95% CI, 1.41-1.56), sequelae of prior trauma (aOR, 1.19; 95% CI, 1.02-1.39), and postprocedural complications (aOR, 1.26; 95% CI, 1.12-1.42). Conclusions and Relevance In this observational study, unhoused status was independently associated with significantly higher odds of 30-day readmission following trauma, often for new injury or poor healing. Improved discharge planning, continuity of care, and access to housing and postacute services are needed.
{"title":"The Experience of Readmission After Trauma Among the Unhoused","authors":"John F. Jachimiak, Yasmin Arda, Carly C. Amon, Riley B. Brackin, Joshua S. Ng-Kamstra, John O. Hwabejire, Haytham M. A. Kaafarani, George C. Velmahos, Michael P. DeWane","doi":"10.1001/jamasurg.2025.5282","DOIUrl":"https://doi.org/10.1001/jamasurg.2025.5282","url":null,"abstract":"Importance Unhoused individuals face significant structural barriers to postacute recovery following traumatic injury. However, national estimates of trauma readmission risk in this population remain limited. This study aimed to evaluate the association between unhoused status and 30-day hospital readmission after trauma. Objective To assess if unhoused status is linked to higher 30-day readmission rates after traumatic injury. Design, Setting, and Participants This retrospective cohort study used data from the 2017 through 2019 National Readmission Database. These data included a national, population-based sample of hospitalizations in the US. Participants included adults aged 18 years or older who were admitted for traumatic injury, identified using <jats:italic toggle=\"yes\">International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)</jats:italic> diagnosis codes. Elective admissions, in-hospital deaths, and discharges in December were excluded, given the 30-day outcome. Unhoused status was defined using <jats:italic toggle=\"yes\">ICD-10-CM</jats:italic> code Z59.0. A total of 2 663 876 trauma admissions were included, of whom 46 381 were unhoused (1.7%). Patients were stratified by housing status based on the <jats:italic toggle=\"yes\">ICD-10</jats:italic> code for homelessness. The primary outcome was 30-day all-cause readmission. Multivariable logistic regression and marginal effects models estimated adjusted odds ratios (aOR) and predicted probabilities, controlling for demographic and clinical factors. Results Unhoused patients were substantially younger (65 years, 10.4% vs 59.4%), predominantly male (77.8% vs 48.2% female), and had much higher rates of substance use disorders (alcohol, 41.3% vs 9.9%; drug, 38.4% vs 5.1%) compared with housed patients; all comparisons were statistically significant ( <jats:italic toggle=\"yes\">P</jats:italic> &amp;lt; .001). The 30-day readmission rate was significantly higher among unhoused patients (19.3% vs 12.2%; <jats:italic toggle=\"yes\">P</jats:italic> &amp;lt; .001), with increased adjusted odds of readmission on multivariable analysis (aOR, 1.63; 95% CI, 1.58-1.67). Against medical advice discharge carried the highest readmission risk among unhoused patients (predicted probability, 30.3%; aOR, 1.81; 95% CI, 1.67-1.96). Unhoused patients were more likely to be readmitted for new traumatic injuries (aOR, 1.48; 95% CI, 1.41-1.56), sequelae of prior trauma (aOR, 1.19; 95% CI, 1.02-1.39), and postprocedural complications (aOR, 1.26; 95% CI, 1.12-1.42). Conclusions and Relevance In this observational study, unhoused status was independently associated with significantly higher odds of 30-day readmission following trauma, often for new injury or poor healing. Improved discharge planning, continuity of care, and access to housing and postacute services are needed.","PeriodicalId":14690,"journal":{"name":"JAMA surgery","volume":"28 1","pages":""},"PeriodicalIF":16.9,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1001/jamasurg.2025.5275
Joanelle A Bailey,Nina E Glass,Cherisse Berry
{"title":"Housing as a Health Intervention-Structural Vulnerability in Trauma.","authors":"Joanelle A Bailey,Nina E Glass,Cherisse Berry","doi":"10.1001/jamasurg.2025.5275","DOIUrl":"https://doi.org/10.1001/jamasurg.2025.5275","url":null,"abstract":"","PeriodicalId":14690,"journal":{"name":"JAMA surgery","volume":"113 1","pages":""},"PeriodicalIF":16.9,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1001/jamasurg.2025.5128
Samantha L Savitch,Tyler M Bauer,Nicole M Mott,Jonathan E Williams,Pasithorn A Suwanabol,Kiran H Lagisetty
{"title":"Smoking and Failure to Rescue From Pulmonary Complications After Lung Resection.","authors":"Samantha L Savitch,Tyler M Bauer,Nicole M Mott,Jonathan E Williams,Pasithorn A Suwanabol,Kiran H Lagisetty","doi":"10.1001/jamasurg.2025.5128","DOIUrl":"https://doi.org/10.1001/jamasurg.2025.5128","url":null,"abstract":"","PeriodicalId":14690,"journal":{"name":"JAMA surgery","volume":"98 1","pages":""},"PeriodicalIF":16.9,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1001/jamasurg.2025.5179
Sean Perez,Adir Mancebo,Patricia Lopez,Leslie Joe,Paul Benavidez,Zhihan Li,Mehri Sadri,Eduardo Spiegel-Pinzon,Ryan Lopez,Bryan Clary,Christopher A Longhurst,Kristin Mekeel,Karandeep Singh
ImportanceThe substantial variation and excess of supplies requested by surgeons for each case using surgical preference cards represents an opportunity for cost reduction through optimization.ObjectiveTo optimize preference cards based on historical supply use captured through surgical receipts.Design, Setting, and ParticipantsThis quality improvement study took place in a large, tertiary, multi-hospital academic health system from January 1, 2019, through December 31, 2023. It included urology, colorectal, and surgical oncology services. These data were analyzed from January 2024 to August 2024.ExposuresSeparate linear time-series ordinary least squares regression models were fit for each surgical receipt item to estimate the optimal number of that item based on data from past cases between January 1, 2019, and December 31, 2023. Optimal surgical preference cards were constructed and compared after collating item-level estimates by optimizing items listed on existing surgical preference cards, creating new preference cards for each procedure, and creating new preference cards that stratify existing preference cards by procedure.Main outcome and measuresThe number of unique and total items on the cards before and after optimization were calculated at the 3 levels. Baseline waste was estimated in existing preference cards as the difference between the total cost of all items on the current surgical preference card and total cost of the surgical receipt associated with the case, averaged across all eligible cases from January 1, 2024, to May 31, 2024. Baseline waste was also compared against the estimated waste, using the optimized surgical preference card at each of the 3 levels.ResultsA total of 1298 preference cards and 432 procedures were evaluated, accounting for 3088 unique preference card-procedure combinations. The current surgical preference cards incurred a mean (SD) cost per case of unused items of $1294.41 ($2307.17), amounting to $3 716 251.11 across all cases in the study. All 3 optimization strategies reduced the cost of unused items and produced less intraoperative burden. The greatest relative reduction in the cost of unused items was seen in colorectal surgery, where cost savings of $488 774.88 reflected a 55.8% reduction.Conclusions and RelevanceOptimization of surgical preference cards with regression models has the potential to reduce surgical waste, with the greatest reduction in waste seen with optimizing existing cards after stratifying at the procedure level.
{"title":"Data and the Art of Surgical Preference Card Maintenance.","authors":"Sean Perez,Adir Mancebo,Patricia Lopez,Leslie Joe,Paul Benavidez,Zhihan Li,Mehri Sadri,Eduardo Spiegel-Pinzon,Ryan Lopez,Bryan Clary,Christopher A Longhurst,Kristin Mekeel,Karandeep Singh","doi":"10.1001/jamasurg.2025.5179","DOIUrl":"https://doi.org/10.1001/jamasurg.2025.5179","url":null,"abstract":"ImportanceThe substantial variation and excess of supplies requested by surgeons for each case using surgical preference cards represents an opportunity for cost reduction through optimization.ObjectiveTo optimize preference cards based on historical supply use captured through surgical receipts.Design, Setting, and ParticipantsThis quality improvement study took place in a large, tertiary, multi-hospital academic health system from January 1, 2019, through December 31, 2023. It included urology, colorectal, and surgical oncology services. These data were analyzed from January 2024 to August 2024.ExposuresSeparate linear time-series ordinary least squares regression models were fit for each surgical receipt item to estimate the optimal number of that item based on data from past cases between January 1, 2019, and December 31, 2023. Optimal surgical preference cards were constructed and compared after collating item-level estimates by optimizing items listed on existing surgical preference cards, creating new preference cards for each procedure, and creating new preference cards that stratify existing preference cards by procedure.Main outcome and measuresThe number of unique and total items on the cards before and after optimization were calculated at the 3 levels. Baseline waste was estimated in existing preference cards as the difference between the total cost of all items on the current surgical preference card and total cost of the surgical receipt associated with the case, averaged across all eligible cases from January 1, 2024, to May 31, 2024. Baseline waste was also compared against the estimated waste, using the optimized surgical preference card at each of the 3 levels.ResultsA total of 1298 preference cards and 432 procedures were evaluated, accounting for 3088 unique preference card-procedure combinations. The current surgical preference cards incurred a mean (SD) cost per case of unused items of $1294.41 ($2307.17), amounting to $3 716 251.11 across all cases in the study. All 3 optimization strategies reduced the cost of unused items and produced less intraoperative burden. The greatest relative reduction in the cost of unused items was seen in colorectal surgery, where cost savings of $488 774.88 reflected a 55.8% reduction.Conclusions and RelevanceOptimization of surgical preference cards with regression models has the potential to reduce surgical waste, with the greatest reduction in waste seen with optimizing existing cards after stratifying at the procedure level.","PeriodicalId":14690,"journal":{"name":"JAMA surgery","volume":"6 1","pages":""},"PeriodicalIF":16.9,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1001/jamasurg.2025.5162
George Ferzli,Yannis Karamitas,Damien Lazar
{"title":"Safeguarding Laparoscopic Training in the Robotic Era.","authors":"George Ferzli,Yannis Karamitas,Damien Lazar","doi":"10.1001/jamasurg.2025.5162","DOIUrl":"https://doi.org/10.1001/jamasurg.2025.5162","url":null,"abstract":"","PeriodicalId":14690,"journal":{"name":"JAMA surgery","volume":"97 1","pages":""},"PeriodicalIF":16.9,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1001/jamasurg.2025.5176
Ashley Y Williams,Joshua L J Jones,Daphney R Portis
{"title":"Zeroing in on Firearm Injury Prevention Efforts-Practice and Policy.","authors":"Ashley Y Williams,Joshua L J Jones,Daphney R Portis","doi":"10.1001/jamasurg.2025.5176","DOIUrl":"https://doi.org/10.1001/jamasurg.2025.5176","url":null,"abstract":"","PeriodicalId":14690,"journal":{"name":"JAMA surgery","volume":"193 1","pages":""},"PeriodicalIF":16.9,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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