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Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer.

ABSTRACT: Objective:To demonstrate the usefulness of applying supervised machine-learning analyses to identify specific groups of patients that experience high levels of mortality post-interhospital transfer. Methods:This was a cross-sectional analysis of data from the Health Care Utilization Project 2013 National Inpatient Sample, that applied supervised machine-learning approaches that included (1) classification and regression tree to identify mutually exclusive groups of patients and their associated characteristics of those experiencing the highest levels of mortality and (2) random forest to identify the relative importance of each characteristic's contribution to post-transfer mortality. Results:A total of 21 independent groups of patients were identified, with 13 of those groups exhibiting at least double the national average rate of mortality post-transfer. Patient characteristics identified as influencing post-transfer mortality the most included: diagnosis of a circulatory disorder, comorbidity of coagulopathy, diagnosis of cancer, and age. Conclusions:Employing supervised machine-learning analyses enabled the computational feasibility to assess all potential combinations of available patient characteristics to identify groups of patients experiencing the highest rates of mortality post-interhospital transfer, providing potentially useful data to support developing clinical decision support systems in future work.

SUBMITTER: Reimer AP

PROVIDER: S-EPMC6425528 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer.

Reimer Andrew P AP Schiltz Nicholas K NK Ho Vanessa P VP Madigan Elizabeth A EA Koroukian Siran M SM

Biomedical informatics insights 20190318

<h4>Objective</h4>To demonstrate the usefulness of applying supervised machine-learning analyses to identify specific groups of patients that experience high levels of mortality post-interhospital transfer.<h4>Methods</h4>This was a cross-sectional analysis of data from the Health Care Utilization Project 2013 National Inpatient Sample, that applied supervised machine-learning approaches that included (1) classification and regression tree to identify mutually exclusive groups of patients and th ...[more]

PMID: 30911219

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Project description:ImportanceAlthough patients with emergency general surgery (EGS) conditions frequently undergo interhospital transfers, the transfer patterns and associated factors are not well understood.ObjectiveTo examine whether patients with EGS conditions are consistently directed to hospitals with more resources and better outcomes.Design, setting, and participantsThis cohort study performed a network analysis of interhospital transfers among adults with EGS conditions from January 1 to December 31, 2016. The analysis used all-payer claims data from the 2016 Healthcare Cost and Utilization Project state inpatient and emergency department databases in 8 states. A total of 728 hospitals involving 85 415 transfers of 80 307 patients were included. Patients were eligible for inclusion if they were 18 years or older and had an acute care hospital encounter with a diagnosis of an EGS condition as defined by the American Association for the Surgery of Trauma. Data were analyzed from January 1, 2020, to June 17, 2021.ExposuresHospital-level measures of size (total bed capacity), resources (intensive care unit [ICU] bed capacity, teaching status, trauma center designation, and presence of trauma and/or surgical critical care fellowships), EGS volume (annual EGS encounters), and EGS outcomes (risk-adjusted failure to rescue and in-hospital mortality).Main outcomes and measuresThe main outcome was hospital-level centrality ratio, defined as the normalized number of incoming transfers divided by the number of outgoing transfers. A higher centrality ratio indicated more incoming transfers per outgoing transfer. Multivariable regression analysis was used to test the hypothesis that a higher hospital centrality ratio would be associated with more resources, higher volume, and better outcomes.ResultsAmong 80 307 total patients, the median age was 63 years (interquartile range [IQR], 50-75 years); 52.1% of patients were male and 78.8% were White. The median number of outgoing and incoming transfers per hospital were 106 (IQR, 61-157) and 36 (IQR, 8-137), respectively. A higher log-transformed centrality ratio was associated with more resources, such as higher ICU capacity (eg, >25 beds vs 0-10 beds: β = 1.67 [95% CI, 1.16-2.17]; P < .001), and higher EGS volume (eg, quartile 4 [highest] vs quartile 1 [lowest]: β = 0.78 [95% CI, 0-1.57]; P = .01). However, a higher log-transformed centrality ratio was not associated with better outcomes, such as lower in-hospital mortality (eg, quartile 4 [highest] vs quartile 1 [lowest]: β = 0.30 [95% CI, -0.09 to 0.68]; P = .83) and lower failure to rescue (eg, quartile 4 [highest] vs quartile 1 [lowest]: β = -0.50 [95% CI, -1.13 to 0.12]; P = .27).Conclusions and relevanceIn this study, EGS transfers were directed to high-volume hospitals with more resources but were not necessarily directed to hospitals with better clinical outcomes. Optimizing transfer destination in the interhospital transfer network has the potential to improve EGS outcomes.

Project description:RationalePatients with ventilator-dependent respiratory failure have improved outcomes at centers with greater expertise; yet, most patients are not treated in such facilities. Efforts to align care for respiratory failure and hospital capability would necessarily require interhospital transfer.ObjectivesTo characterize the prevalence and the patient and hospital factors associated with interhospital transfer of adults residing in Florida with ventilator-dependent respiratory failure.MethodsWe performed a retrospective, observational study using Florida Healthcare Cost and Utilization Project data. We selected patients 18 years of age and older with International Classification of Diseases, Ninth Revision, codes of respiratory failure and mechanical ventilation during 2012 and 2013, and we identified cohorts of patients that did and did not undergo interhospital transfer. We obtained patient sociodemographic and clinical variables and categorized hospitals into subtypes on the basis of patient volume and services provided: large, medium (nonprofit or for-profit), and small.ResultsInterhospital transfer was our primary outcome measure. Patient sociodemographics, clinical variables, and hospital types were used as covariates. We identified 2,580 patients with ventilator-dependent respiratory failure who underwent interhospital transfer. Overall, transfer was uncommon, with only 2.9% of patients being transferred. In a hierarchical model, age less than 65 years (odds ratio [OR], 2.09; 95% confidence interval [CI], 1.77-2.45) and tracheostomy (OR, 3.19; 95% CI, 2.80-3.65) were associated with higher odds of transfer, whereas having Medicaid was associated with lower odds of transfer than having commercial insurance (OR, 0.65; 95% CI, 0.56-0.75). Additionally, care in medium-sized for-profit hospitals was associated with lower odds of transfer (OR, 1.37 vs. 2.70) than care in medium nonprofit hospitals, despite similar hospital characteristics.ConclusionsIn Florida, interhospital transfer of patients with ventilator-dependent respiratory failure is uncommon and more likely among younger, commercially insured, medically resource-intensive patients. For-profit hospitals are less likely to transfer than nonprofit hospitals. In future studies, researchers should test for geographic variations and examine the clinical implications of selectivity in interhospital transfer of patients with ventilator-dependent respiratory failure.

Project description:BackgroundNational administrative datasets have demonstrated increased risk-adjusted mortality among patients undergoing interhospital transfer (IHT) compared to patients admitted through the emergency department (ED).ObjectiveTo investigate the impact of patient-level data not available in larger administrative datasets on the association between IHT status and in-hospital mortality.DesignRetrospective cohort study with logistic regression analyses to examine the association between IHT status and in-hospital mortality, controlling for covariates that were potential confounders. Model 1: IHT status, admit service. Model 2: model 1 and patient demographics. Model 3: model 2 and disease-specific conditions. Model 4: model 3 and vital signs and laboratory data.ParticipantsNine thousand three hundred twenty-eight adults admitted to Medicine services.Main measuresInterhospital transfer status, coded as an unordered categorical variable (IHT vs ED vs clinic), was the independent variable. The primary outcome was in-hospital mortality. Secondary outcomes included unadjusted length of stay and total cost.Key resultsIHT patients accounted for 180 out of 484 (37%) in-hospital deaths, despite accounting for only 17% of total admissions. Unadjusted mean length of stay was 8.4 days vs 5.6 days (p < 0.0001) and mean total cost was $22,647 vs $12,968 (p < 0.0001) for patients admitted via IHT vs ED respectively. The odds ratios (OR) for in-hospital mortality for patients admitted via IHT compared to the ED were as follows: model 1 OR, 2.06 (95% CI 1.66-2.56, p < 0.0001); model 2 OR, 2.07 (95% CI 1.66-2.58, p < 0.0001); model 3 OR, 2.07 (95% CI 1.63-2.61, p < 0.0001); model 4 OR, 1.70 (95% CI 1.31-2.19, p < 0.0001). The AUCs of the models were as follows: model 1, 0.74; model 2, 0.76; model 3, 0.83; model 4, 0.88, consistent with a good prediction model.ConclusionsPatient-level characteristics affect the association between IHT and in-hospital mortality. After adjusting for patient-level clinical characteristics, IHT status remains associated with in-hospital mortality.

Project description:BackgroundSuboptimal triage of critically ill patients with surgical sepsis may contribute to adverse outcomes. Patients transferred to a tertiary care center after spending 24 hours or longer at an outside facility were compared with patients who had early triage to a tertiary care center with the null hypothesis that management parameters and outcomes would be similar between groups.MethodsThis prospective observational cohort study included 308 patients treated for surgical sepsis in a surgical intensive care unit at a tertiary care center. Patients transferred after spending more than 24 hours at an outside facility (n = 69) were compared with patients who were directly admitted or transferred within 24 hours (n = 239). Patient characteristics, management parameters, and outcomes were compared between groups. This study was registered at ClinicalTrials.gov (NCT02276066).ResultsAverage outside facility length of stay in the delayed transfer group was 43 hours. Delayed transfer patients had higher sequential organ failure assessment (7 vs. 5, p = 0.003) and APACHE II scores (19 vs. 16, p = 0.007) on admission. The interval between admission and source control was significantly longer in the delayed transfer group (12.1 hours vs. 1.0 hours, p = 0.009). The incidence of nosocomial infection was significantly higher in the delayed transfer group (41% vs. 23%, p = 0.005). Delayed transfer was independently associated with a 10-day increase in hospital length of stay. Delayed transfer patients were less likely to be discharged home (22% vs. 59%, p < 0.001) and suffered twofold higher in-hospital mortality (14.5% vs. 7.1%, p = 0.056).ConclusionPatients with surgical sepsis who spent more than 24 hours at an outside facility prior to transfer had greater initial illness severity, longer intervals between admission and source control, and more nosocomial infections compared with patients who had early triage to a tertiary care center.Level of evidenceCare management/therapeutic, Level IV; Epidemiologic/prognostic, Level III.

Dataset Information

Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer.

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Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer.

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