• 임상간호연구
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• 제19권2호
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• pp.265-274
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• 2013

Purpose: The aim of this study was to determine the risk factors contributed to unplanned readmission to intensive care unit (ICU) and to investigate the prediction model of unplanned readmission. Methods: We retrospectively reviewed the electronic medical records which included the data of 3,903 patients who had discharged from ICUs in a university hospital in Seoul from January 2011 to April 2012. Results: The unplanned readmission rate was 4.8% (n=186). The nine variables were significantly different between the unplanned readmission and no readmission groups: age, clinical department, length of stay at 1st ICU, operation, use of ventilator during 24 hours a day, APACHE II score at ICU admission and discharge, direct nursing care hours and Glasgow coma scale total score at 1st ICU discharge. The clinical department, length of stay at 1st ICU, operation and APACHE II score at ICU admission were the significant predictors of unplanned ICU readmission. The predictive model's area under the curve was .802 (p<.001). Conclusion: We identified the risk factors and the prediction model associated with unplanned ICU readmission. Better patient assessment tools and knowledge about risk factors could contribute to reduce unplanned ICU readmission rate and mortality.

• 한국의료질향상학회지
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• 제4권2호
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• pp.242-259
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• 1997

This study describes associated factors of readmission of 213 inpatients from an university hospital in Seoul. This retrospective study reviewed medical records of patients who discharged from a hospital stay for general diseases between 1 August 1995 and 31 October 1995, Cases were 68 discharge patients with an unplanned readmission within 30 days of discharge from an index stay. And the other cases are 145 patients who had more than two discharges and didn't have an unplanned readmission within 30 days. Logistic regression model was analyzed and the results were as follows; 1. duration of readmission, rate of unpayed, room, path, and risk of disease were more likely to be readmitted unexpectedly than the expected readmission patients. 2. early readmission, low risk condition group, and inadquateness of discharge plann for patients had unplanned radmissions rather than planned readmissions. Therefore, discharge planning education to health care provider is required and assessement of discharge planning should be evaluated. Readmissions are usually for related problems that arose during the original hopitaliztion and caused cost problems. Especially the unplanned readmissions are frequently preventable. Ultimately, models for readmissions can serve as a valuable clinical tool for target high-risk patients and older patients and with this kind of tools we can reduce hospital readmissions and maintain high-quality of inpatient care.

• 한국보건간호학회지
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• 제16권1호
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• pp.201-212
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• 2002

This study was designed to identify the risk factors of unplanned readmission in a university hospital. The six-month discharge information from January to June, 2000 in a tertiary university hospital was used as a source of data through the medical record and hospital information system. To increase the effect of comparison. the data were collected by sampling 192 couples (384 patients) of unplanned readmission group through the matching by its disease groups, sex, and age. The accuracy of prediction for unplanned readmission was analyzed by constructing the predicted model of unplanned readmission through the logistic regression. The study results are as follows. The conditional logistic regression analysis was performed with nine variables at the significance level 0.05 through univariate analysis including residence, days after discharge, initial admission route, previous admission, transfer to special care unite, hospital stay days, medical care expenses, special cares, and laboratory and imaging services. As a result, the closer the patients live in Seoul and Gyeong-in area (Odds ratio=2.529, p=0.003), the shorter the days after discharge was (Odds ratio=0.600, p=0.000), and the more frequent admission rate was (Odds ratio=2.317, p=0.004), the more unplanned readmission was resulted. Also, the accuracy of prediction for data classification of this regression model showed $70.3\%$(032+83/306).

• 대한간호학회지
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• 제50권6호
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• pp.787-798
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• 2020

Purpose: This study aimed to assign weights for subscales and items of the Post-Intensive Care Syndrome questionnaire and suggest optimal cut-off values for screening unplanned hospital readmissions of critical care survivors. Methods: Seventeen experts participated in an analytic hierarchy process for weight assignment. Participants for cut-off analysis were 240 survivors who had been admitted to intensive care units for more than 48 hours in three cities in Korea. We assessed participants using the 18-item Post-Intensive Care Syndrome questionnaire, generated receiver operating characteristic curves, and analysed cut-off values for unplanned readmission based on sensitivity, specificity, and positive likelihood ratios. Results: Cognitive, physical, and mental subscale weights were 1.13, 0.95, and 0.92, respectively. Incidence of unplanned readmission was 25.4%. Optimal cut-off values were 23.00 for raw scores and 23.73 for weighted scores (total score 54.00), with an area of under the curve (AUC) of .933 and .929, respectively. There was no significant difference in accuracy for original and weighted scores. Conclusion: The optimal cut-off value accuracy is excellent for screening of unplanned readmissions. We recommend that nurses use the Post-Intensive Care Syndrome Questionnaire to screen for readmission risk or evaluating relevant interventions for critical care survivors.

• 한국의료질향상학회지
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• 제10권1호
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• pp.28-41
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• 2003

Background : Because unplanned readmissions to intensive care unit(ICU)might be related with undesirable patient outcomes, we investigated the pattern of and reason for unplanned ICU readmission to provide baseline data for reducing unplanned returns to ICU. Methods : The subjects included all patients who readmitted to ICU during the same hospitalization at a tertiary referral hospital between January 1st and June 30th 2002. Quality improvement(QI) nurse collected the data through medical records and a medical director reviewed the data collected. Results : 1) The average unplanned ICU readmission rate was 5.6%(gastroenterology 14.6%, pediatrics 12.7%, pulmonology 11.9%, neurosurgery 6.3%, general surgery 5.3%, chest surgery 3.9%, and cardiology 3.3%). 2) Among the unplanned readmissions, more than 50% of cases were from patients older than 60 years, and the main categories of diagnose at hospital admission were neurologic disease(29.9%) and cardiovascular disease(27.6%). 3) Of unplanned ICU readmissions, 41.8% had recurrence of the initial problems, 44.8% had occurrence of new problems. And 9.7% required post-operative care after unplanned operations. 4) The most common cause responsible for unplanned ICU readmission were respiratory problem(38.3%) and cardiovascular problem(14.3%). 5) About 40% of unplanned ICU readmission occurred within 3 days after ICU discharge. 6) Average length of stay of the readmitted patients to ICUs were much longer than that of non-readmitted patients. 7) Hospital mortality rate was much higher for unplanned ICU readmitted patients(23.6%) than for non-readmitted patients(1.5%) (P<0.001). Conclusions : This study showed that the unplanned ICU readmitted patients had poor outcomes(high morality and increased length of stay). In addition study results suggest that more attention should be paid to patients in ICU with poor respiratory function or elderly patients, and careful clinical decisions are required at discharged from ICU to general ward.

• 중환자간호학회지
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• 제11권3호
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• pp.45-57
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• 2018

Purpose : This study was performed to identify the influencing factors of unplanned intensive care unit (ICU) readmission. Methods : The study adopted a Rretrospective case control cohort design. Data were collected from the electronic medical records of 844 patients who had been discharged from the ICUs of a university hospital in Incheon from June 2014 to December 2014. Results : The study found the unplanned ICU readmission rate was to be 6.4%(n=54). From the univariate analysis revealed that, major symptoms at $1^{st}$ ICU admission, severity at $1^{st}$ ICU admission (CPSCS and APACHE II), duration of applying ventilator application during $1^{st}$ ICU admission, severity at $1^{st}$ discharge from ICU (CPSCS, APACHE II, and GCS), and application of $FiO_2$ with oxygen therapy, implementation of sputum expectoration methods, and length of stay of ICU at $1^{st}$ ICU discharge were appeared to be significant; further, decision tree model analysis revealed that while only 4 variables (sputum expectoration methods, length of stay of ICU, $FiO_2$ with oxygen therapy at $1^{st}$ ICU discharge, and major symptoms at $1^{st}$ ICU admission) were shown to be significant. Conclusions : Since sputum expectoration method was the most important factor to predictor of unplanned ICU readmission, a assessment tool for the patients' capability of sputum expectoration needs to should be developed and implemented, and standardized ICU discharge criteria, including the factors identified from the by empirical evidences, might should be developed to decrease the unplanned ICU readmission rate.

• Archives of Plastic Surgery
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• 제41권2호
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• pp.116-121
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• 2014

Background Understanding risk factors that increase readmission rates may help enhance patient education and set system-wide expectations. We aimed to provide benchmark data on causes and predictors of readmission following inpatient plastic surgery. Methods The 2011 National Surgical Quality Improvement Program dataset was reviewed for patients with both "Plastics" as their recorded surgical specialty and inpatient status. Readmission was tracked through the "Unplanned Readmission" variable. Patient characteristics and outcomes were compared using chi-squared analysis and Student's t-tests for categorical and continuous variables, respectively. Multivariate regression analysis was used for identifying predictors of readmission. Results A total of 3,671 inpatient plastic surgery patients were included. The unplanned readmission rate was 7.11%. Multivariate regression analysis revealed a history of chronic obstructive pulmonary disease (COPD) (odds ratio [OR], 2.01; confidence interval [CI], 1.12- 3.60; P=0.020), previous percutaneous coronary intervention (PCI) (OR, 2.69; CI, 1.21-5.97; P=0.015), hypertension requiring medication (OR, 1.65; CI, 1.22-2.24; P<0.001), bleeding disorders (OR, 1.70; CI, 1.01-2.87; P=0.046), American Society of Anesthesiologists (ASA) class 3 or 4 (OR, 1.57; CI, 1.15-2.15; P=0.004), and obesity (body mass index ${\geq}30$) (OR, 1.43; CI, 1.09-1.88, P=0.011) to be significant predictors of readmission. Conclusions Inpatient plastic surgery has an associated 7.11% unplanned readmission rate. History of COPD, previous PCI, hypertension, ASA class 3 or 4, bleeding disorders, and obesity all proved to be significant risk factors for readmission. These findings will help to benchmark inpatient readmission rates and manage patient and hospital system expectations.

• 한국병원경영학회지
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• 제15권4호
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• pp.125-142
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• 2010

Objective : To determine demographic, clinical, health care utilization factors predicting unplanned readmission(within 28 days) to the hospital. Methods : A case-control study was conducted from January to December 2009. Multiple logistic regression was used to examine risk factors for readmission. 180 patients who had been readmitted within 28 days and 1,784 controls were recruited from an university hospital in Seoul. Results : Six risk factors associated with readmission risk were identified and include mail sex, medical service rather than surgical service, number of comorbid diseases, type of patient's room, lenth of stay, number of admissions in the prior 12 months. Conclusions : One of the association with readmission risk identified was the number of hospital admissions in the previous year. This factor may be the only risk factor necessary for assessing prior risk and has the additional advantage of being easily accessible from computerized medical records without requiring other medical record review. This risk factor may be useful in identifying a group at high readmission risk, which could be targeted in intervention studies. Multiple risk factors intervention approach should be considered in designing future prevention strategies.

• 한국콘텐츠학회논문지
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• 제22권2호
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• pp.794-802
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• 2022

본 연구의 목적은 심장판막 수술 받은 환자를 대상으로 전자의무기록 검토를 통해 퇴원 후 30일 내 비계획적 재입원군의 특성과, 재입원 영향요인을 규명하고자 하는 후향적 조사연구이다. 대상자는 S시에 위치한 상급종합병원에서 2018년 1월부터 2019년 8월까지 심장판막수술을 받고 포함기준에 맞는 퇴원환자 423명이었다. 비계획적 재입원 대상자는 총 48명(11.3%)이었고, 그 원인은 심방세동 13건 (27.1%)과 수술부위 통증이 13건(27.1%)으로 가장 많았고, 와파린의 치료농도가 안 맞는 경우 10건 (20.8%), 전신 위약감 7건 (14.6%). 저혈압 5건 (10.4%). 심낭삼출 4건 (8.3%), 수술상처 감염 3건 (6.3%), 출혈 3건 (6.3%), 고열 3건 (6.3%), 뇌경색 1건(2.1%)이었다. 재입원에 영향을 미치는 변수는 암의 병력 (OR=2.60, 95% CI 1.13-6.03, p=.025), 퇴원 후 거주지는 병원이 아닌 집으로 간 경우 (OR=2.91, 95% CI 1.33-6.36, p=.008), 판막수술종류로서 승모판막성형술이 대동맥판막 치환술보다 재입원률이 높았다(OR=1.21, 95% CI 1.21-4.98, p=.012). 비계획적 재입원 감소를 위해 환자와 돌봄 제공자에게 퇴원 전 만성질환병력관리와 재입원의 위험 요인을 미리 사정하고 병원 방문할 수 있도록 하는 개별화된 교육프로그램이 필요하다.

• 한국농촌간호학회지
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• 제19권1호
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• pp.44-54
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• 2024

Purpose: This study identifies the factors influencing unplanned readmissions among participants of the medical aid community care pilot program. Methods: This descriptive study analyzed data from 1,013 participants in a medical aid community care pilot program. Data were analyzed using multiple logistic regression analysis. Results: The presence of mental illness, injury-related conditions, long-term care grades, and activities of daily living scores are key factors influencing the likelihood of readmission. In particular, the presence of a mental disorder or an injury-related condition increased the probability of readmission, whereas individuals with long-term care grades 1~2 showed a decreased likelihood of readmission. Conclusion: This study emphasizes the importance of enhancing the management of mental and injury-related conditions, effective utilization of long-term care services, and improvement of ADL scores to reduce readmission. These findings offer crucial insights for enhancing the efficiency of home medical care benefit programs and sustainable expansion of services.