A Predictive Model of Intrinsic Factors Associated with Long-Stay Nursing Home Care After Hospitalization

2020 ◽  
pp. 105477382098527
Author(s):  
Jane Flanagan ◽  
Marie Boltz ◽  
Ming Ji
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Predictive Model ◽  
Operating Characteristic ◽  
Resource Planning ◽  
Term Care ◽  
Intrinsic Factors ◽  
Patients At Risk ◽  
Receiver Operating

We aimed to build a predictive model with intrinsic factors measured upon admission to skilled nursing facilities (SNFs) post-acute care (PAC) to identify older adults transferred from SNFs to long-term care (LTC) instead of home. We analyzed data from Massachusetts in 23,662 persons admitted to SNFs from PAC in 2013. Explanatory logistic regression analysis identified single “intrinsic predictors” related to LTC placement. To assess overfitting, the logistic regression predictive model was cross-validated and evaluated by its receiver operating characteristic (ROC) curve. A 12-variable predictive model with “intrinsic predictors” demonstrated both high in-sample and out-of-sample predictive accuracy in the receiver operating characteristic ROC and area under the ROC among patients at risk of LTC placement. This predictive model may be used for early identification of patients at risk for LTC after hospitalization in order to support targeted rehabilitative approaches and resource planning.

scholarly journals Predicting Risk of Postoperative Lung Injury in High-risk Surgical Patients

2014 ◽  
Vol 120 (5) ◽  
pp. 1168-1181 ◽  
Author(s):  
Daryl J. Kor ◽  
Ravi K. Lingineni ◽  
Ognjen Gajic ◽  
Pauline K. Park ◽  
James M. Blum ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Surgical Patients ◽  
Patients At Risk ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract Background: Acute respiratory distress syndrome (ARDS) remains a serious postoperative complication. Although ARDS prevention is a priority, the inability to identify patients at risk for ARDS remains a barrier to progress. The authors tested and refined the previously reported surgical lung injury prediction (SLIP) model in a multicenter cohort of at-risk surgical patients. Methods: This is a secondary analysis of a multicenter, prospective cohort investigation evaluating high-risk patients undergoing surgery. Preoperative ARDS risk factors and risk modifiers were evaluated for inclusion in a parsimonious risk-prediction model. Multiple imputation and domain analysis were used to facilitate development of a refined model, designated SLIP-2. Area under the receiver operating characteristic curve and the Hosmer–Lemeshow goodness-of-fit test were used to assess model performance. Results: Among 1,562 at-risk patients, ARDS developed in 117 (7.5%). Nine independent predictors of ARDS were identified: sepsis, high-risk aortic vascular surgery, high-risk cardiac surgery, emergency surgery, cirrhosis, admission location other than home, increased respiratory rate (20 to 29 and ≥30 breaths/min), Fio2 greater than 35%, and Spo2 less than 95%. The original SLIP score performed poorly in this heterogeneous cohort with baseline risk factors for ARDS (area under the receiver operating characteristic curve [95% CI], 0.56 [0.50 to 0.62]). In contrast, SLIP-2 score performed well (area under the receiver operating characteristic curve [95% CI], 0.84 [0.81 to 0.88]). Internal validation indicated similar discrimination, with an area under the receiver operating characteristic curve of 0.84. Conclusions: In this multicenter cohort of patients at risk for ARDS, the SLIP-2 score outperformed the original SLIP score. If validated in an independent sample, this tool may help identify surgical patients at high risk for ARDS.

scholarly journals Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients

2021 ◽  
Author(s):  
Victor Alfonso Rodriguez ◽  
Shreyas Bhave ◽  
Ruijun Chen ◽  
Chao Pang ◽  
George Hripcsak ◽  
...  
Keyword(s):  
At Risk ◽  
Mechanical Ventilation ◽  
Renal Replacement Therapy ◽  
Receiver Operating Characteristic ◽  
Replacement Therapy ◽  
Operating Characteristic ◽  
Prediction Models ◽  
External Validation ◽  
Electronic Health Record Data ◽  
Receiver Operating

Abstract Objective Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. Materials and Methods For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output. Results The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. Discussion Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. Conclusions We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

Evidence for circulating microRNA hsa-let-7d-3p as a potential new biomarker for sepsis in human subject

2020 ◽  
Author(s):  
Zhaorui Zhang ◽  
Hailun Luo ◽  
Chunsun Li ◽  
Zhixin Liang
Keyword(s):  
Logistic Regression ◽  
Early Detection ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Quantitative Polymerase Chain Reaction ◽  
Multiple Logistic Regression Analysis ◽  
Circulating Microrna ◽  
Qrt Pcr ◽  
Serum Mirna ◽  
Receiver Operating

Abstract Background: Current biomarkers for early detection of sepsis have low sensitivity and specificity. Serum microRNAs (miRNAs) have been proposed as novel non-invasive biomarkers for various diseases. The aim of the present study was to discover a novel diagnostic biomarker for sepsis in human subjects. Methods: miRNA expression profile was performed using peripheral blood from three sepsis patients in sepsis stage and condition improved stage using microarray screening. The differentially expressed miRNAs were primary validated by real-time quantitative polymerase chain reaction (qRT-PCR) in a further set of 20 sepsis patients in the sepsis stage and condition improved stage. We finally validate the different expressed miRNA in 95 sepsis patients and 66 non sepsis patients. The validated miRNAs along with patients’ clinical indictors were analyzed in a multivariate logistic regression model. The diagnosis value of the changed miRNA in sepsis was determined and compared with CRP and WBC by analyzing the receiver operating characteristic (ROC) curves. Results: According to the criteria we detected 3 miRNAs up regulated and 8 miRNAs down regulated by miRNA chip. qRT-PCR detection showed that the expression of hsa-let-7d-3p in sepsis patient was up regulated compared with non-sepsis patients. In a multiple logistic regression analysis, serum miRNA hsa-let-7d-3p was found to be independent predictor of sepsis. Receiver operating characteristic curve (ROC) analysis showed that the area under ROC curve of serum miRNA hsa-let-7d-3p was 0.696 (95% confidence interval [0.615, 0.778]). Conclusion: miRNA hsa-let-7d-3p was identified as novel biomarkers for the early detection of sepsis.

scholarly journals Predictive Factors of Severe Coronavirus Disease 2019 in Previously Healthy Young Adults: A Single-Center, Retrospective Study

2020 ◽  
Author(s):  
Changzhi Zhou ◽  
Zhe Huang ◽  
Yi Hu ◽  
Shuang Geng ◽  
Weijun Tan ◽  
...  
Keyword(s):  
Young Adults ◽  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Clinical Outcomes ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Tertiary Hospital ◽  
Factors Affecting ◽  
Severe Group ◽  
Receiver Operating

Abstract Background: Several previously healthy young adults have developed Coronavirus Disease 2019 (COVID-19), and a few of them progressed severe COVID-19. However, the factors are not yet determined.Method: We retrospectively analyzed 123 previously healthy young adults diagnosed with COVID-19 from January 2020 to March 2020 in a tertiary hospital in Wuhan. Patients were classified as having mild or severe COVID-19 based on their respiratory rate, SpO2 and PaO2/FiO2 levels. Patients' symptoms, computer tomography (CT) images, preadmission drugs received and the admission serum biochemical examination were compared between the mild and severe group. Significant variables were enrolled logistic regression model to predict the factors affecting disease outcomes. A receiver operating characteristic (ROC) curve was applied to validate the predictive value of predictors.Result: Age; temperature; anorexia; and white blood cell count, neutrophil percentage, platelet count, lymphocyte count, C-reactive protein, aspartate transaminase, creatine kinase, albumin, and fibrinogen values were significantly different between patients with mild and severe COVID-19 (P<0.05). Logistic regression analysis confirmed that lymphopenia (P=0.010) indicated poor clinical outcomes in previously healthy young adults with COVID-19, with area under the receiver operating characteristic curve (AUC) was 0.791(95%CI 0.704–0.877)(P<0.001).Conclusion: For previously healthy young adults with COVID-19, lymphopenia on admission can predict poor clinical outcomes.

Applying an Evidence-Based Assessment Model to Identify Students at Risk for Perceived Academic Problems following Concussion

2016 ◽  
Vol 22 (10) ◽  
pp. 1038-1049 ◽  
Author(s):  
Danielle M. Ransom ◽  
Alison R. Burns ◽  
Eric A. Youngstrom ◽  
Christopher G. Vaughan ◽  
Maegan D. Sady ◽  
...  
Keyword(s):  
At Risk ◽  
Receiver Operating Characteristic ◽  
Symptom Severity ◽  
Operating Characteristic ◽  
Executive Dysfunction ◽  
Evidence Based ◽  
Academic Problems ◽  
Post Injury ◽  
Students At Risk ◽  
Receiver Operating

AbstractObjectives: The aim of this study was to demonstrate the utility of an evidence-based assessment (EBA) model to establish a multimodal set of tools for identifying students at risk for perceived post-injury academic problems. Methods: Participants included 142 students diagnosed with concussion (age: M=14.95; SD=1.80; 59% male), evaluated within 4 weeks of injury (median=16 days). Demographics, pre-injury history, self- and parent-report measures assessing symptom severity and executive functions, and cognitive test performance were examined as predictors of self-reported post-injury academic problems. Results: Latent class analysis categorized participants into “high” (44%) and “low” (56%) levels of self-reported academic problems. Receiver operating characteristic analyses revealed significant discriminative validity for self- and parent-reported symptom severity and executive dysfunction and self-reported exertional response for identifying students reporting low versus high academic problems. Parent-reported symptom ratings [area under the receiver operating characteristic curve (AUC)=.79] and executive dysfunction (AUC=.74), and self-reported ratings of executive dysfunction (AUC=.84), symptoms (AUC=.80), and exertional response (AUC=.70) each classified students significantly better than chance (ps<.001). Hierarchical logistic regression indicated that, of the above, self-reported symptoms and executive dysfunction accounted for the most variance in the prediction of self-reported academic problems. Conclusions: Post-concussion symptom severity and executive dysfunction significantly predict perceived post-injury academic problems. EBA modeling identified the strongest set of predictors of academic challenges, offering an important perspective in the management of concussion by applying traditional strengths of neuropsychological assessment to clinical decision making. (JINS, 2016, 22, 1038–1049)

scholarly journals Pretransplant HbA1c Is a Useful Predictor for the Development of New-Onset Diabetes in Renal Transplant Recipients Receiving No or Low-Dose Erythropoietin

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Kazuaki Tokodai ◽  
Noritoshi Amada ◽  
Izumi Haga ◽  
Atsushi Nakamura ◽  
Toshiaki Kashiwadate ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Low Dose ◽  
Renal Transplant Recipients ◽  
Characteristic Analysis ◽  
Onset Diabetes ◽  
Hba1c Levels ◽  
New Onset ◽  
Receiver Operating

Aims. To evaluate the predictive power of pretransplant HbA1c for new-onset diabetes after transplantation (NODAT) in kidney transplant candidates, who had several predispositions for fluctuated HbA1c levels.Methods. We performed a retrospective study of 119 patients without diabetes who received kidney transplantation between March 2000 and January 2012. Univariate and multivariate logistic regression analyses were used to investigate the association of several parameters with NODAT. Predictive discrimination of HbA1c was assessed using a receiver-operating characteristic curve.Results. Seventeen patients (14.3%) developed NODAT within 1 year of transplantation. Univariate logistic regression analysis revealed that recipient age, gender, and HbA1c were predictors of NODAT. In the multivariate analysis, the association between pretransplant HbA1c and NODAT development did not reach statistical significance (P=0.07). To avoid the strong influence of high-dose erythropoietin on HbA1c levels, we performed subgroup analyses on 85 patients receiving no or low-dose (≤6000 IU/week) erythropoietin. HbA1c was again an independent predictor for NODAT. Receiver-operating characteristic analysis revealed a cut-off value of 5.2% with an optimal sensitivity of 64% and specificity of 78% for predicting NODAT.Conclusions. Our results reveal that the pretransplant HbA1c level is a useful predictor for NODAT in patients receiving no or low-dose erythropoietin.

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