scholarly journals Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

10.2196/16306 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e16306
Author(s):  
Peng Zhao ◽  
Illhoi Yoo ◽  
Syed H Naqvi
Keyword(s):  
Risk Factors ◽  
Protective Factors ◽  
Hospital Readmission ◽  
Inpatient Care ◽  
Early Stage ◽  
Multivariate Logistic Regression Analysis ◽  
Day Hospital ◽  
Multivariate Logistic Regression ◽  
Data Set ◽  
Readmission Risk

Background Existing readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians’ impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available. Objective The objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors. Methods We extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors. Results We developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported. Conclusions The performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

scholarly journals A Novel Prediction Model for Long-Term SARS-CoV-2 RNA Shedding in Non-death Hospitalized Patients with COVID-19: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Xiaoli Lei ◽  
Junli Wang ◽  
Lijie Kou ◽  
Zhigang Yang
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Predictive Model ◽  
Goodness Of Fit ◽  
Multivariate Logistic Regression Analysis ◽  
Hospitalized Patients ◽  
Multivariate Logistic Regression ◽  
Data Set

Abstract Background: Because of the lack of compelling evidence for predicting the duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA shedding, the purpose of this retrospective study was to establish a predictive model for long-term SARS-CoV-2 RNA shedding in non-death hospitalized patients with coronavirus disease-19 (COVID-19).Methods: 97 non-death hospitalized patients with COVID-19 admitted to two hospitals in Henan province of China from February 3, 2020 to March 31, 2020 were retrospectively enrolled. Multivariate logistic regression was performed to identify the high risk factors associated with long-term SARS-CoV-2 RNA shedding and a predictive model was established and represented by a nomogram. Its performance was assessed with discrimination and calibration.Results: 97 patients were divided into the long-term (>21 days) group (n = 27, 27.8%) and the short-term (≤ 21 days) group (n = 70, 72.2%) based on their viral shedding duration. Multivariate logistic regression analysis showed that time from illness onset to diagnosis (OR 1.224, 95% CI 1.070-1.400, P = 0.003) and interstitial opacity in chest computerized tomography(CT) scan (OR 6.516, 95% CI 2.041-20.798, P = 0.002) were independent risk factors for long-term SARS-CoV-2 RNA shedding. A prediction model, which is presented with a nomogram, was established by incorporating the two risk factors. The goodness-of-fit statistics for the nomogram was not statistically significant (χ2 = 8.292; P = 0.406), and its area under the receiver operator characteristic curve was 0.834 (95% CI 0.731- 0.936; P < 0.001).Conclusion: The established model has a good predictive performance on the long-term viral RNA shedding in non-death hospitalized patients with COVID-19, but it still needs further validation by independent data set of large samples in the future.

scholarly journals Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

2019 ◽  
Author(s):  
Sameh N. Saleh ◽  
Anil N. Makam ◽  
Ethan A. Halm ◽  
Oanh Kieu Nguyen
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Risk Prediction ◽  
Hospital Readmission ◽  
Model Performance ◽  
Risk Prediction Model ◽  
Day Hospital ◽  
Baseline Characteristics ◽  
Substantial Change ◽  
Readmission Risk

AbstractDespite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day readmission prediction model predicts 7-day readmissions. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We compared model performance and compared differences in strength of model factors between the 7-day model to the 30-day model. While there was no substantial change in model performance between the original 30-day and the re-derived 7-day model, there was significant change in strength of predictors. Characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to the day of discharge.

scholarly journals Psychosocial Factors and 30-Day Hospital Readmission among Individuals Receiving Maintenance Dialysis: A Prospective Study

2017 ◽  
Vol 45 (5) ◽  
pp. 400-408 ◽  
Author(s):  
Jennifer E. Flythe ◽  
Johnathan Hilbert ◽  
Abhijit V. Kshirsagar ◽  
Constance A. Gilet
Keyword(s):  
Risk Factors ◽  
Social Support ◽  
Depressive Symptoms ◽  
Health Literacy ◽  
Hospital Readmission ◽  
Day Hospital ◽  
Multivariable Model ◽  
Dialysis Patients ◽  
Self Rated Health ◽  
Readmission Risk

Background: Thirty-day hospital readmissions are common among maintenance dialysis patients. Prior studies have evaluated easily measurable readmission risk factors such as comorbid conditions, laboratory results, and hospital discharge day. We undertook this prospective study to investigate the associations between hospital-assessed depression, health literacy, social support, and self-rated health (separately) and 30-day hospital readmission among dialysis patients. Methods: Participants were recruited from the University of North Carolina Hospitals, 2014-2016. Validated depression, health literacy, social support, and self-rated health screening instruments were administered during index hospitalizations. Multivariable logistic regression models with 30-day readmission as the dependent outcome were used to examine readmission risk factors. Results: Of the 154 participants, 58 (37.7%) had a 30-day hospital readmission. In unadjusted analyses, individuals with positive screening for depression, lower health literacy, and poorer social support were more likely to have a 30-day readmission (vs. negative screening). Positive depression screening and poorer social support remained significantly associated with 30-day readmission in models adjusted for race, heart failure, admitting service, weekend discharge day, and serum albumin: adjusted OR (95% CI) 2.33 (1.02-5.15) for positive depressive symptoms and 2.57 (1.10-5.91) for poorer social support. The area under the receiver operating characteristic curve (AUC) of the multivariable model adjusted for social support status was significantly greater than the AUC of the multivariable model without social support status (test for equality; p value = 0.04). Conclusion: Poor social support and depressive symptoms identified during hospitalizations may represent targetable readmission risk factors among dialysis patients. Our findings suggest that hospital-based assessments of select psychosocial factors may improve readmission risk prediction.

scholarly journals Patient-Related Risk Factors for Unplanned 30-Day Hospital Readmission Following Primary and Revision Total Knee Arthroplasty: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 134
Author(s):  
Daniel Gould ◽  
Michelle M Dowsey ◽  
Tim Spelman ◽  
Olivia Jo ◽  
Wassif Kabir ◽  
...  
Keyword(s):  
Risk Factors ◽  
Total Knee Arthroplasty ◽  
Knee Arthroplasty ◽  
Hospital Readmission ◽  
Day Hospital ◽  
Post Discharge ◽  
Comorbidity Burden ◽  
Total Knee ◽  
The Impact ◽  
Readmission Risk

Total knee arthroplasty (TKA) is a highly effective procedure for advanced osteoarthritis of the knee. Thirty-day hospital readmission is an adverse outcome related to complications, which can be mitigated by identifying associated risk factors. We aimed to identify patient-related characteristics associated with unplanned 30-day readmission following TKA, and to determine the effect size of the association between these risk factors and unplanned 30-day readmission. We searched MEDLINE and EMBASE from inception to 8 September 2020 for English language articles. Reference lists of included articles were searched for additional literature. Patients of interest were TKA recipients (primary and revision) compared for 30-day readmission to any institution, due to any cause, based on patient risk factors; case series were excluded. Two reviewers independently extracted data and carried out critical appraisal. In-hospital complications during the index admission were the strongest risk factors for 30-day readmission in both primary and revision TKA patients, suggesting discharge planning to include closer post-discharge monitoring to prevent avoidable readmission may be warranted. Further research could determine whether closer monitoring post-discharge would prevent unplanned but avoidable readmissions. Increased comorbidity burden correlated with increased risk, as did specific comorbidities. Body mass index was not strongly correlated with readmission risk. Demographic risk factors included low socioeconomic status, but the impact of age on readmission risk was less clear. These risk factors can also be included in predictive models for 30-day readmission in TKA patients to identify high-risk patients as part of risk reduction programs.

scholarly journals Risk factors for tissue expander infection in scar reconstruction: a retrospective cohort study of 2374 consecutive cases

2020 ◽  
Vol 8 ◽  
Author(s):  
Chen Dong ◽  
Minhui Zhu ◽  
Luguang Huang ◽  
Wei Liu ◽  
Hengxin Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Treatment Failure ◽  
Logistic Regression Analysis ◽  
Disease Duration ◽  
Multivariate Logistic Regression Analysis ◽  
Tissue Expansion ◽  
Multivariate Logistic Regression ◽  
Hematoma Evacuation

Abstract Background Tissue expansion is used for scar reconstruction owing to its excellent clinical outcomes; however, the complications that emerge from tissue expansion hinder repair. Infection is considered a major complication of tissue expansion. This study aimed to analyze the perioperative risk factors for expander infection. Methods A large, retrospective, single-institution observational study was carried out over a 10-year period. The study enrolled consecutive patients who had undergone tissue expansion for scar reconstruction. Demographics, etiological data, expander-related characteristics and postoperative infection were assessed. Univariate and multivariate logistic regression analysis were performed to identify risk factors for expander infection. In addition, we conducted a sensitivity analysis for treatment failure caused by infection as an outcome. Results A total of 2374 expanders and 148 cases of expander infection were assessed. Treatment failure caused by infection occurred in 14 expanders. Multivariate logistic regression analysis identified that disease duration of ≤1 year (odds ratio (OR), 2.07; p &lt; 0.001), larger volume of expander (200–400 ml vs &lt;200 ml; OR, 1.74; p = 0.032; &gt;400 ml vs &lt;200 ml; OR, 1.76; p = 0.049), limb location (OR, 2.22; p = 0.023) and hematoma evacuation (OR, 2.17; p = 0.049) were associated with a high likelihood of expander infection. Disease duration of ≤1 year (OR, 3.88; p = 0.015) and hematoma evacuation (OR, 10.35; p = 0.001) were so related to high risk of treatment failure. Conclusions The rate of expander infection in patients undergoing scar reconstruction was 6.2%. Disease duration of &lt;1 year, expander volume of &gt;200 ml, limb location and postoperative hematoma evacuation were independent risk factors for expander infection.

Risk Factors for Aortic Valve Calcification in Patients on Regular Hemodialysis

2009 ◽  
Vol 32 (3) ◽  
pp. 173-179 ◽  
Author(s):  
Dejan Petrović ◽  
Radmila Obrenović ◽  
Biljana Stojimirović
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Risk Factor ◽  
Aortic Valve ◽  
Independent Risk Factor ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Aortic Valve Calcification ◽  
Valve Calcification ◽  
Regular Hemodialysis

Introduction Aortic valve calcification (AVC) accelerates development of aortic valve stenosis and cardiovascular complications. Hyperphosphatemia is one of the key risk factors for aortic valve calcification. Aim The aim of this study was to evaluate the prevalence of AVC in patients on regular hemodialysis and to assess the impact of different factors on its appearance. Method: The study investigated a total of 115 patients treated in the Hemodialysis Department of the Urology and Nephrology Clinic at the Kragujevac Clinical Center in Serbia. The variables investigated were: serum albumin, C-reactive protein (CRP), homocysteine, total cholesterol, LDL-cholesterol (LDL-C), HDL-cholesterol (HDL-C), triglycerides (TG), Apolipoprotein A-I (Apo A-I), Apolipoprotein B (Apo B) and lipoprotein (a), calcium, phosphate and parathormone, and calcium-phosphorus product (Ca × P). Patients were evaluated by echocardiography for AVC. Statistical analysis included univariate and multivariate logistic regression analysis. Results Univariate regression analysis showed that serum phosphate levels and Ca × P are the most important risk factors for AVC (p<0.001). Multivariate logistic regression analysis revealed that hyperphosphatemia is an independent risk factor for AVC (p<0.001). Conclusion Hyperphosphatemia is an independent risk factor for aortic valve calcification.

scholarly journals Predictive risk factors for worse outcomes in COVID-19 patients with different clinical features at baseline

2021 ◽  
Author(s):  
Elisabetta Schiaroli ◽  
Anna Gidari ◽  
Giovanni Brachelente ◽  
Sabrina Bastianelli ◽  
Alfredo Villa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Critical Illness ◽  
Serum Ferritin ◽  
Logistic Regression Analysis ◽  
Severe Disease ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Neutrophil Lymphocyte Ratio

IntroductionCOVID-19 is characterized by a wide range of clinical expression and by possible progression to critical illness and death. Therefore it is essential to identify risk factors predicting progression towards serious and fatal diseases. The aim of our study was to identify laboratory predictive markers of clinical progression in patients with moderate/severe disease and in those with acute respiratory distress syndrome (ARDS).Material and methodsUsing electronic medical records for all demographic, clinical and laboratory data, a retrospective study on all consecutive patients with COVID-19 admitted to the Infectious Disease Clinic of Perugia was performed. The PaO2/FiO2 ratio (P/F) assessment cut‑off of 200 mm Hg was used at baseline to categorize the patients into two clinical groups. The progression towards invasive ventilation and/or death was used to identify critical outcome. Statistical analysis was performed. Multivariate logistic regression analysis was adopted to identify risk factors of critical illness and mortality.ResultsIn multivariate logistic regression analysis neutrophil/lymphocyte ratio (NLR) was the only significant predictive factor of progression to a critical outcome (p = 0.03) and of in-hospital mortality (p = 0.03). In ARDS patients no factors were associated with critical progression. Serum ferritin > 1006 ng/ml was the only predictive value of critical outcome in COVID-19 subjects with moderate/severe disease (p = 0.02).ConclusionsNeutrophil/lymphocyte ratio and serum ferritin are the only biomarkers that can help to stratify the risk of severity and mortality in patients with COVID-19.

scholarly journals Risk Factors and Outcomes of Hospital Acquired Pneumonia in Young Bangladeshi Children

Life ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1030
Author(s):  
Abu Sadat Mohammad Sayeem Bin Shahid ◽  
Tahmina Alam ◽  
Lubaba Shahrin ◽  
K. M. Shahunja ◽  
Md. Tanveer Faruk ◽  
...  
Keyword(s):  
Risk Factors ◽  
Severe Acute Malnutrition ◽  
Multivariate Logistic Regression Analysis ◽  
Acute Malnutrition ◽  
Persistent Diarrhea ◽  
Multivariate Logistic Regression ◽  
Resource Limited ◽  
Hospital Acquired Pneumonia ◽  
Independent Risk Factors ◽  
Hospital Acquired

Hospital acquired pneumonia (HAP) is common and often associated with high mortality in children aged five or less. We sought to evaluate the risk factors and outcome of HAP in such children. We compared demographic, clinical, and laboratory characteristics in children <5 years using a case control design during the period of August 2013 and December 2017, where children with HAP were constituted as cases (n = 281) and twice as many randomly selected children without HAP were constituted as controls (n = 562). HAP was defined as a child developing a new episode of pneumonia both clinically and radiologically after at least 48 h of hospitalization. A total of 4101 children were treated during the study period. The mortality was significantly higher among the cases than the controls (8% vs. 4%, p = 0.014). In multivariate logistic regression analysis, after adjusting for potential confounders, it was found that persistent diarrhea (95% CI = 1.32–5.79; p = 0.007), severe acute malnutrition (95% CI = 1.46–3.27; p < 0.001), bacteremia (95% CI = 1.16–3.49; p = 0.013), and prolonged hospitalization of >5 days (95% CI = 3.01–8.02; p < 0.001) were identified as independent risk factors for HAP. Early identification of these risk factors and their prompt management may help to reduce HAP-related fatal consequences, especially in resource limited settings.

scholarly journals Factors associated with depression in the patients with diabetes mellitus type 2

2019 ◽  
Vol 76 (11) ◽  
pp. 1178-1183 ◽  
Author(s):  
Admir Sabanovic ◽  
Natasa Maksimovic ◽  
Mirjana Stojanovic-Tasic ◽  
Marijan Bakic ◽  
Anita Grgurevic
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Diabetes Mellitus Type 2 ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Factors Associated

Background/Aim. The assessment of association of depression and diabetes mellitus type 2 using the Patient Health Questionaire (PHQ-9) has not been done in Montenegro. The aim of this study was to assess the prevalence of depression in the patients with type 2 diabetes mellitus, and to identify the risk factors associated with the presence of depression. Methods. A cross-sectional study was conducted at the General Hospital in Bijelo Polje, from July to September, 2015. It included 70 patients over 35 years of age with the diagnosis of diabetes for at least six months. For the assessment of depression presence and intensity PHQ?9 was used. All variables associated with the presence of depression at a significance level of p < 0.05 were included into the final method of the multivariate logistic regression analysis. Results. Comorbidities were statistically significant more frequent among patients with depression (?2 = 5.40; p = 0.020). Duration of diabetes over five years was significantly associated with depression (?2 = 12.48; p < 0.001). Depression occurred more frequently among physically inactive subjects (?2 = 10.74; p = 0.005). The presence of diabetic polyneuropathy (?2 = 6.04; p = 0.014) and cataract (?2 = 5.351; p = 0.021) were also significantly associated with depression. A multivariate logistic regression analysis showed that the duration of diabetes over five years and presence of cataract were independently associated with depression. Conclusion. The risk factors for depression among the subjects with diabetes were disease duration more than five years and the presence of cataract. Since depression is a serious disease and can be a risk factor for many chronic diseases, the best way of prevention is its early detection and treatment.

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