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Beals J, Barnes JJ, Durand DJ, Rimar JM, Donohue TJ, Hoq SM, Belk KW, Amin AN, Rothman MJ. Stratifying Deterioration Risk by Acuity at Admission Offers Triage Insights for Coronavirus Disease 2019 Patients. Crit Care Explor 2021; 3:e0400. [PMID: 33937866 PMCID: PMC8084057 DOI: 10.1097/cce.0000000000000400] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES Triaging patients at admission to determine subsequent deterioration risk can be difficult. This is especially true of coronavirus disease 2019 patients, some of whom experience significant physiologic deterioration due to dysregulated immune response following admission. A well-established acuity measure, the Rothman Index, is evaluated for stratification of patients at admission into high or low risk of subsequent deterioration. DESIGN Multicenter retrospective study. SETTING One academic medical center in Connecticut, and three community hospitals in Connecticut and Maryland. PATIENTS Three thousand four hundred ninety-nine coronavirus disease 2019 and 14,658 noncoronavirus disease 2019 adult patients admitted to a medical service between January 1, 2020, and September 15, 2020. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Performance of the Rothman Index at admission to predict in-hospital mortality or ICU utilization for both general medical and coronavirus disease 2019 populations was evaluated using the area under the curve. Precision and recall for mortality prediction were calculated, high- and low-risk thresholds were determined, and patients meeting threshold criteria were characterized. The Rothman Index at admission has good to excellent discriminatory performance for in-hospital mortality in the coronavirus disease 2019 (area under the curve, 0.81-0.84) and noncoronavirus disease 2019 (area under the curve, 0.90-0.92) populations. We show that for a given admission acuity, the risk of deterioration for coronavirus disease 2019 patients is significantly higher than for noncoronavirus disease 2019 patients. At admission, Rothman Index-based thresholds segregate the majority of patients into either high- or low-risk groups; high-risk groups have mortality rates of 34-45% (coronavirus disease 2019) and 17-25% (noncoronavirus disease 2019), whereas low-risk groups have mortality rates of 2-5% (coronavirus disease 2019) and 0.2-0.4% (noncoronavirus disease 2019). Similarly large differences in ICU utilization are also found. CONCLUSIONS Acuity level at admission may support rapid and effective risk triage. Notably, in-hospital mortality risk associated with a given acuity at admission is significantly higher for coronavirus disease 2019 patients than for noncoronavirus disease 2019 patients. This insight may help physicians more effectively triage coronavirus disease 2019 patients, guiding level of care decisions and resource allocation.
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Affiliation(s)
| | - Jaime J Barnes
- Department of Medicine, Sinai Hospital of Baltimore, Baltimore, MD
| | - Daniel J Durand
- Department of Innovation and Research, LifeBridge Health, Baltimore, MD
| | - Joan M Rimar
- Yale New Haven Health System, Yale New Haven Hospital, New Haven, CT
| | - Thomas J Donohue
- Yale New Haven Health System, Yale New Haven Hospital, New Haven, CT
| | - S Mahfuz Hoq
- Yale New Haven Health System, Bridgeport Hospital, Bridgeport, CT
| | | | - Alpesh N Amin
- Irvine Medical Center, The University of California, Orange, CA
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Gotur DB, Masud F, Paranilam J, Zimmerman JL. Analysis of Rothman Index Data to Predict Postdischarge Adverse Events in a Medical Intensive Care Unit. J Intensive Care Med 2018; 35:606-610. [PMID: 29720051 DOI: 10.1177/0885066618770128] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Currently, there are no objective metrics included in the intensive care unit (ICU) discharge decision making process. In this study, we evaluate Rothman Index(RI) data for a possible metric as part of a quality improvement project. Our objectives were to determine whether RI could predict adverse events occurring within 72 hours of ICU discharge decision, the optimal clinical cutoff value for this metric, and to determine whether there is a relation between the RI warning alert 24 hours prior to discharge and adverse events postdischarge. DESIGN Retrospective observational study. SETTING Single center tertiary hospital. PATIENTS Adult medical ICU patients discharged from the ICU between January 20, 2015 and March 14, 2015. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS A total of 194 patients were studied with mean age of 62.74 (18.37) years. Data collection included RI at the time of decision-making for ICU discharge and the presence of any warning signals in the previous 24 hours. A 72-hour follow-up chart review recorded any adverse events, including readmission to a higher level of care, discontinuation of discharge due to clinical status change, emergency department visit if discharged home, rapid response activation, or cardiopulmonary arrest postdischarge. Adverse events after ICU discharge were observed in 31 (16%) patients with 9 events being ICU readmission (4.6%). Based on an age-adjusted multivariate model, a higher RI was associated with lower odds of an adverse event (odds ratio [OR] = 0.969, P = .006, confidence interval [CI]: 0.9487-0.9911). An RI value ≥ 50 was associated with 72% lower odds of an adverse event (OR = 0.2887, 95% CI = 0.1278-0.6517 and P = .003) compared to RI < 50. This RI cutoff value was associated with the largest decrease in odds of events. As expected, patients with a very high-risk warning alert had a higher proportion of adverse events compared to patients who did not. (31.75% vs 12.65%, P = < .02). CONCLUSIONS Patients who have an RI < 50 or a very high-risk warning alert have a higher risk of adverse events postdischarge from the ICU. Rothman Index may be a useful metric for ICU discharge decision-making.
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Affiliation(s)
- Deepa Bangalore Gotur
- Department of Medicine, Houston Methodist Hospital, Houston Methodist, Fannin, Houston, Texas, USA
| | - Faisal Masud
- Department of Medicine, Houston Methodist Hospital, Houston Methodist, Fannin, Houston, Texas, USA
| | - Jaya Paranilam
- Department of Medicine, Houston Methodist Hospital, Houston Methodist, Fannin, Houston, Texas, USA
| | - Janice L Zimmerman
- Department of Medicine, Houston Methodist Hospital, Houston Methodist, Fannin, Houston, Texas, USA
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Wengerter BC, Pei KY, Asuzu D, Davis KA. Rothman Index variability predicts clinical deterioration and rapid response activation. Am J Surg 2018; 215:37-41. [DOI: 10.1016/j.amjsurg.2017.07.031] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 06/23/2017] [Accepted: 07/26/2017] [Indexed: 10/19/2022]
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Abstract
BACKGROUND Continuity of nursing care in hospitals remains poor and not prioritized, and we do not know whether discontinuous nursing care is negatively impacting patient outcomes. OBJECTIVES This study aims to examine nursing care discontinuity and its effect on patient clinical condition over the course of acute hospitalization. RESEARCH DESIGN Retrospective longitudinal analysis of electronic health records (EHR). Average point-in-time discontinuity was estimated from time of admission to discharge and compared with theoretical predictions for optimal continuity and random nurse assignment. Mixed-effects models estimated within-patient change in clinical condition following a discontinuity. SUBJECTS A total of 3892 adult medical-surgical inpatients were admitted to a tertiary academic medical center in the Eastern United States during July 1, 2011 and December 31, 2011. MEASURES Exposure: discontinuity of nursing care was measured at each nurse assessment entry into a patient's EHR as assignment of the patient to a nurse with no prior assignment to that patient. OUTCOME patient's clinical condition score (Rothman Index) continuously tracked in the EHR. RESULTS Discontinuity declined from nearly 100% in the first 24 hours to 70% at 36 hours, and to 50% by the 10th postadmission day. Discontinuity was higher than predicted for optimal continuity, but not random. Each instance of discontinuity lead to a 0.12-0.23 point decline in the Rothman Index score, with more pronounced effects for older and high-mortality risk patients. CONCLUSIONS Discontinuity in acute care nurse assignments was high and negatively impacted patient clinical condition. Improved continuity of provider-patient assignment should be advocated to improve patient outcomes in acute care.
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Yakusheva O, Weiss M. Rankings matter: nurse graduates from higher-ranked institutions have higher productivity. BMC Health Serv Res 2017; 17:134. [PMID: 28193208 PMCID: PMC5307737 DOI: 10.1186/s12913-017-2074-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 02/07/2017] [Indexed: 11/29/2022] Open
Abstract
Background Increasing demand for baccalaureate-prepared nurses has led to rapid growth in the number of baccalaureate-granting programs, and to concerns about educational quality and potential effects on productivity of the graduating nursing workforce. We examined the association of individual productivity of a baccalaureate-prepared nurse with the ranking of the degree-granting institution. Methods For a sample of 691 nurses from general medical-surgical units at a large magnet urban hospital between 6/1/2011–12/31/2011, we conducted multivariate regression analysis of nurse productivity on the ranking of the degree-granting institution, adjusted for age, hospital tenure, gender, and unit-specific effects. Nurse productivity was coded as “top”/“average”/“bottom” based on a computation of individual nurse value-added to patient outcomes. Ranking of the baccalaureate-granting institution was derived from the US News and World Report Best Colleges Rankings’ categorization of the nurse’s institution as the “first tier” or the “second tier”, with diploma or associate degree as the reference category. Results Relative to diploma or associate degree nurses, nurses who had attended first-tier universities had three-times the odds of being in the top productivity category (OR = 3.18, p < 0.001), while second-tier education had a non-significant association with productivity (OR = 1.73, p = 0.11). Being in the bottom productivity category was not associated with having a baccalaureate degree or the quality tier. Conclusions The productivity boost from a nursing baccalaureate degree depends on the quality of the educational institution. Recognizing differences in educational outcomes, initiatives to build a baccalaureate-educated nursing workforce should be accompanied by improved access to high-quality educational institutions.
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Affiliation(s)
- Olga Yakusheva
- Department of Systems, Populations, and Leadership, School of Nursing, 400 North Ingalls Street, Suite 4243, Ann Arbor, MI, 48103, USA. .,Department of Health Policy and Management, School of Public Health, 400 North Ingalls Street, Suite 4243, Ann Arbor, MI, 48103, USA. .,Institute for Health Policy Innovation, University of Michigan, 400 North Ingalls Street, Suite 4243, Ann Arbor, MI, 48103, USA.
| | - Marianne Weiss
- Marquette University College of Nursing, 530N 16th St, Milwaukee, WI, 53233, USA
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Rothman MJ, Tepas JJ, Nowalk AJ, Levin JE, Rimar JM, Marchetti A, Hsiao AL. Development and validation of a continuously age-adjusted measure of patient condition for hospitalized children using the electronic medical record. J Biomed Inform 2017; 66:180-193. [DOI: 10.1016/j.jbi.2016.12.013] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 11/26/2016] [Accepted: 12/31/2016] [Indexed: 10/20/2022]
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Sankey CB, McAvay G, Siner JM, Barsky CL, Chaudhry SI. "Deterioration to Door Time": An Exploratory Analysis of Delays in Escalation of Care for Hospitalized Patients. J Gen Intern Med 2016; 31:895-900. [PMID: 26969311 PMCID: PMC4945556 DOI: 10.1007/s11606-016-3654-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2015] [Revised: 09/23/2015] [Accepted: 02/19/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND Timely escalation of care for patients experiencing clinical deterioration in the inpatient setting is challenging. Deterioration on a general floor has been associated with an increased risk of death, and the early period of deterioration may represent a time during which admission to the intensive care unit (ICU) improves survival. Previous studies examining the association between delay from onset of clinical deterioration to ICU transfer and mortality are few in number and were conducted more than 10 years ago. OBJECTIVE We aimed to evaluate the impact of delays in the escalation of care among clinically deteriorating patients in the current era of inpatient medicine. DESIGN AND PARTICIPANTS This was a retrospective cohort study that analyzed data from 793 patients transferred from non-intensive care unit (ICU) inpatient floors to the medical intensive care unit (MICU), from 2011 to 2013 at an urban, tertiary, academic medical center. MAIN MEASURES "Deterioration to door time (DTDT)" was defined as the time between onset of clinical deterioration (as evidenced by the presence of one or more vital sign indicators including respiratory rate, systolic blood pressure, and heart rate) and arrival in the MICU. KEY RESULTS In our sample, 64.6 % had delays in care escalation, defined as greater than 4 h based on previous studies. Mortality was significantly increased beginning at a DTDT of 12.1 h after adjusting for age, gender, and severity of illness. CONCLUSIONS Delays in the escalation of care for clinically deteriorating hospitalized patients remain frequent in the current era of inpatient medicine, and are associated with increased in-hospital mortality. Development of performance measures for the care of clinically deteriorating inpatients remains essential, and timeliness of care escalation deserves further consideration.
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Affiliation(s)
- Christopher B Sankey
- Section of General Medicine, Department of Internal Medicine, Yale University School of Medicine, Harkness Hall A, Room 306, 367 Cedar St., New Haven, CT, 06510, USA. .,Yale-New Haven Hospital, New Haven, CT, USA.
| | - Gail McAvay
- Section of Geriatric Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Jonathan M Siner
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Carol L Barsky
- Patient Safety and Quality, Hackensack University Medical Center, Hackensack, NJ, USA
| | - Sarwat I Chaudhry
- Section of General Medicine, Department of Internal Medicine, Yale University School of Medicine, Harkness Hall A, Room 306, 367 Cedar St., New Haven, CT, 06510, USA.,Yale-New Haven Hospital, New Haven, CT, USA
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Cardona-Morrell M, Hillman K. Development of a tool for defining and identifying the dying patient in hospital: Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL). BMJ Support Palliat Care 2015; 5:78-90. [PMID: 25613983 PMCID: PMC4345773 DOI: 10.1136/bmjspcare-2014-000770] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Revised: 10/23/2014] [Accepted: 11/23/2014] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To develop a screening tool to identify elderly patients at the end of life and quantify the risk of death in hospital or soon after discharge for to minimise prognostic uncertainty and avoid potentially harmful and futile treatments. DESIGN Narrative literature review of definitions, tools and measurements that could be combined into a screening tool based on routinely available or obtainable data at the point of care to identify elderly patients who are unavoidably dying at the time of admission or at risk of dying during hospitalisation. MAIN MEASUREMENTS Variables and thresholds proposed for the Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL screening tool) were adopted from existing scales and published research findings showing association with either in-hospital, 30-day or 3-month mortality. RESULTS Eighteen predictor instruments and their variants were examined. The final items for the new CriSTAL screening tool included: age ≥65; meeting ≥2 deterioration criteria; an index of frailty with ≥2 criteria; early warning score >4; presence of ≥1 selected comorbidities; nursing home placement; evidence of cognitive impairment; prior emergency hospitalisation or intensive care unit readmission in the past year; abnormal ECG; and proteinuria. CONCLUSIONS An unambiguous checklist may assist clinicians in reducing uncertainty patients who are likely to die within the next 3 months and help initiate transparent conversations with families and patients about end-of-life care. Retrospective chart review and prospective validation will be undertaken to optimise the number of prognostic items for easy administration and enhanced generalisability. Development of an evidence-based tool for defining and identifying the dying patient in hospital: CriSTAL.
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Affiliation(s)
- Magnolia Cardona-Morrell
- The Simpson Centre for Health Services Research, South Western Sydney Clinical School, The University of New South Wales, Kensington, NSW 2052, Australia
| | - Ken Hillman
- The Simpson Centre for Health Services Research, South Western Sydney Clinical School, The University of New South Wales & Liverpool Hospital, Liverpool BC 1871, New South Wales, Australia
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Yakusheva O, Lindrooth R, Weiss M. Nurse value-added and patient outcomes in acute care. Health Serv Res 2014; 49:1767-86. [PMID: 25256089 DOI: 10.1111/1475-6773.12236] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The aims of the study were to (1) estimate the relative nurse effectiveness, or individual nurse value-added (NVA), to patients' clinical condition change during hospitalization; (2) examine nurse characteristics contributing to NVA; and (3) estimate the contribution of value-added nursing care to patient outcomes. DATA SOURCES/STUDY SETTING Electronic data on 1,203 staff nurses matched with 7,318 adult medical-surgical patients discharged between July 1, 2011 and December 31, 2011 from an urban Magnet-designated, 854-bed teaching hospital. STUDY DESIGN Retrospective observational longitudinal analysis using a covariate-adjustment value-added model with nurse fixed effects. DATA COLLECTION/EXTRACTION METHODS Data were extracted from the study hospital's electronic patient records and human resources databases. PRINCIPAL FINDINGS Nurse effects were jointly significant and explained 7.9 percent of variance in patient clinical condition change during hospitalization. NVA was positively associated with having a baccalaureate degree or higher (0.55, p = .04) and expertise level (0.66, p = .03). NVA contributed to patient outcomes of shorter length of stay and lower costs. CONCLUSIONS Nurses differ in their value-added to patient outcomes. The ability to measure individual nurse relative value-added opens the possibility for development of performance metrics, performance-based rankings, and merit-based salary schemes to improve patient outcomes and reduce costs.
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Affiliation(s)
- Olga Yakusheva
- Division of Systems Leadership and Effectiveness Science, School of Nursing, Department of Health Management and Policy, School of Public Health, University of Michigan, 400 North Ingalls Building, Ann Arbor, MI, 48109-5482
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Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Aff (Millwood) 2014; 33:1123-31. [DOI: 10.1377/hlthaff.2014.0041] [Citation(s) in RCA: 640] [Impact Index Per Article: 64.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- David W. Bates
- David W. Bates ( ) is chief of the Division of General Medicine, Brigham and Women’s Hospital, in Boston, Massachusetts
| | - Suchi Saria
- Suchi Saria is an assistant professor of computer science and health policy management at the Center for Population Health and IT, Johns Hopkins University, in Baltimore, Maryland
| | - Lucila Ohno-Machado
- Lucila Ohno-Machado is associate dean for informatics and technology in the Division of Biomedical Informatics, University of California, San Diego, in La Jolla
| | - Anand Shah
- Anand Shah is vice president of clinical services at PCCI, in Dallas, Texas
| | - Gabriel Escobar
- Gabriel Escobar is regional director of hospital operations research and director of the Systems Research Initiative, Division of Research, Kaiser Permanente, in Oakland, California
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Bittleman DB, Solinger AB, Finlay GD. Shared decision-making at end-of-life is aided by graphical trending of illness severity. BMJ Case Rep 2014; 2014:bcr-2013-201522. [PMID: 24419639 DOI: 10.1136/bcr-2013-201522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
The Rothman Index (RI) gives a visual picture of patient's condition and progress for the physician and family to view together. This case demonstrates how the RI graph facilitates physician-family communication. An 85-year-old man with normal pressure hydrocephalus and ventriculoperitoneal shunt presented with a subdural haematoma. He required a temporoparietal craniotomy and evacuation of left subdural haematoma, followed by care in an intensive inpatient rehabilitation unit. His course was complicated by aspiration pneumonia, dehydration, renal failure and phenytoin toxicity. During hospitalisation, the patient's RI graph was reviewed daily with his family. The RI provided an unambiguous visualisation of the trend of patient acuity, which depicted the patient's persistent decline in health, and made clear to the family the situation of the patient. This clarity was instrumental in prompting frank discussions of prognosis and consideration of comfort measures, resulting in timely transfer to hospice.
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Bradley EH, Yakusheva O, Horwitz LI, Sipsma H, Fletcher J. Identifying patients at increased risk for unplanned readmission. Med Care 2013; 51:761-6. [PMID: 23942218 PMCID: PMC3771868 DOI: 10.1097/mlr.0b013e3182a0f492] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Reducing readmissions is a national priority, but many hospitals lack practical tools to identify patients at increased risk of unplanned readmission. OBJECTIVE To estimate the association between a composite measure of patient condition at discharge, the Rothman Index (RI), and unplanned readmission within 30 days of discharge. SUBJECTS Adult medical and surgical patients in a major teaching hospital in 2011. MEASURES The RI is a composite measure updated regularly from the electronic medical record based on changes in vital signs, nursing assessments, Braden score, cardiac rhythms, and laboratory test results. We developed 4 categories of RI and tested its association with readmission within 30 days, using logistic regression, adjusted for patient age, sex, insurance status, service assignment (medical or surgical), and primary discharge diagnosis. RESULTS Sixteen percent of the sample patients (N=2730) had an unplanned readmission within 30 days of discharge. The risk of readmission for a patient in the highest risk category (RI<70) was >1 in 5 while the risk of readmission for patients in the lowest risk category was about 1 in 10. In multivariable analysis, patients with an RI<70 (the highest risk category) or 70-79 (medium risk category) had 2.65 (95% confidence interval, 1.72-4.07) and 2.40 (95% confidence interval, 1.57-3.67) times higher odds of unplanned readmission, respectively, compared with patients in the lowest risk category. CONCLUSION Clinicians can use the RI to help target hospital programs and supports to patients at highest risk of readmission.
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Affiliation(s)
- Elizabeth H Bradley
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT 06520, USA.
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