scholarly journals Activities of Daily Living (ADL) Predicts in-hospital Mortality in Geriatric Patients with Community-acquired Pneumonia (CAP)

2020 ◽  
Author(s):  
Yu Kang ◽  
Xiang-Yang Fang ◽  
dong wang ◽  
Xiao-juan Wang
Keyword(s):  
Elderly Patients ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic ◽  
Barthel Index ◽  
Daily Living ◽  
Geriatric Patients ◽  
Operating Characteristic ◽  
Community Acquired Pneumonia ◽  
Increased Risk ◽  
Receiver Operating

Abstract Background: Community-acquired pneumonia (CAP) is an important problem with significant mortality. Activity of daily living (ADL) function decline is associated with increased mortality in elderly patients. We aimed to investigate the prognostic value of ADL at admission on the in-hospital mortality in geriatric patients with pneumonia. Methods: Patients over 65 years old admitted to Beijing Chao-yang hospital due to CAP from June 2012 through June 2020 were retrospectively reviewed by electronic medical records. Risk factors for mortality in pneumonia patients described in literature were included in our study. ADL evaluation at admission was performed by Barthel index (BI). Results: 4880 patients were included, 131 patients (2.7%) died during their admission. 69.5% patients in Dead group had a BI scores < 60. Mean BI score in the Dead group and Alive group were 49.89±30.20 and 81.57±22.14, respectively. Dead group had lower BI scores than Alive group (p＜0.001). A low BI was associated with increased in-hospital mortality. Logistic regression analyses demonstrated that ADL function at admission was significantly and independently associated with the in-hospital mortality, either in younger (age 65-74years) or very elderly (age≥75years) patients. Receiver operating characteristic ( ROC ) curve analysis revealed that BI at admission is an predictor related to in-hospital mortality in elderly patients, The area under the receiver operating characteristic (ROC) curves of BI in predicting in-hospital mortality was 0.81 (with 95% confidence interval: 0.78–0.85).Conclusion: ADL decline is associated with increased risk of in-hospital mortality among elderly patients hospitalized with CAP. ADL function at admission can predict in-hospital mortality in geriatric patients with CAP. Barthel Index (BI) can be used as a simple and convenient method for the assessment of the ADL functional status at admission in geriatric patients with CAP to identify patients at high risk and conducive to clinical decision making.

scholarly journals Cut-off Values of the Respiratory Muscle Power and Peak Cough Flow in Post-Stroke Dysphagia

Medicina ◽  
2020 ◽  
Vol 56 (12) ◽  
pp. 635
Author(s):  
Yeon Jae Han ◽  
Jungjae Lee ◽  
Dong Gyun Sohn ◽  
Geun-Young Park ◽  
Youngkook Kim ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Multivariable Analysis ◽  
Post Stroke ◽  
Increased Risk ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background and objectives: This study aimed to determine the cut-off values of the following three respiratory pressure meters; the voluntary peak cough flow (PCF), maximal expiratory pressure (MEP) and maximal inspiratory pressure (MIP); associated with post-stroke dysphagia and assess which of these parameters show good diagnostic properties associated with post-stroke dysphagia. Materials and Methods: Retrospective analysis of a prospectively maintained database. Records of patients with first-ever diagnosed dysphagia attributable to cerebrovascular disease, who had performed spirometry measurements for the PCF, MIP and MEP. Results: From a total of 237 stroke patients, 163 patients were diagnosed with dysphagia. Those with dysphagia had significantly lower PCF values than those without dysphagia (116.3 ± 75.3 vs. 219.4 ± 91.8 L/min, p < 0.001). In addition, the former group also had lower MIP (30.5 ± 24.7 vs. 41.6 ± 25.7 cmH2O, p = 0.0002) and MEP (41.0 ± 27.9 vs. 62.8 ± 32.3 cmH2O, p < 0.001) values than the latter group. The receiver operating characteristic curve analysis showed that the PCF cut-off value of 151 L/min (area under the receiver operating characteristic curve [AUC] 0.81; sensitivity 72%; specificity 78.8%) was associated with post-stroke dysphagia. The optimum MEP and MIP cut-off were 38 cmH2O (AUC 0.70, sensitivity 58%; specificity 77.7%) and 20 cmH2O (AUC 0.65, sensitivity 49%; specificity 84%). PCF showed the highest AUC results. Results from the univariate analysis indicated that PCF values of ≤151 L/min increased risk of dysphagia by 9.51-fold (4.96–18.23). Multivariable analysis showed that after controlling of other clinical factor, the PCFs at this cut-off value still showed increased risk of by 4.19 (2.02–83.69) but this was not observed with the MIPs or MEPs. Conclusions: Our study has provided cut-off values that are associated with increased risk of dysphagia. Among the three parameters, PCF showed increased association with post-stroke dysphagia.

scholarly journals Development and validation of a scoring system for mortality prediction and application of standardized W statistics to assess the performance of emergency departments

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinwoo Jeong ◽  
Sung Woo Lee ◽  
Won Young Kim ◽  
Kap Su Han ◽  
Su Jin Kim ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Scoring System ◽  
Operating Characteristic ◽  
Mortality Prediction ◽  
Mortality Prediction Model ◽  
National Emergency ◽  
Receiver Operating

Abstract Background In-hospital mortality and short-term mortality are indicators that are commonly used to evaluate the outcome of emergency department (ED) treatment. Although several scoring systems and machine learning-based approaches have been suggested to grade the severity of the condition of ED patients, methods for comparing severity-adjusted mortality in general ED patients between different systems have yet to be developed. The aim of the present study was to develop a scoring system to predict mortality in ED patients using data collected at the initial evaluation and to validate the usefulness of the scoring system for comparing severity-adjusted mortality between institutions with different severity distributions. Methods The study was based on the registry of the National Emergency Department Information System, which is maintained by the National Emergency Medical Center of the Republic of Korea. Data from 2016 were used to construct the prediction model, and data from 2017 were used for validation. Logistic regression was used to build the mortality prediction model. Receiver operating characteristic curves were used to evaluate the performance of the prediction model. We calculated the standardized W statistic and its 95% confidence intervals using the newly developed mortality prediction model. Results The area under the receiver operating characteristic curve of the developed scoring system for the prediction of mortality was 0.883 (95% confidence interval [CI]: 0.882–0.884). The Ws score calculated from the 2016 dataset was 0.000 (95% CI: − 0.021 – 0.021). The Ws score calculated from the 2017 dataset was 0.049 (95% CI: 0.030–0.069). Conclusions The scoring system developed in the present study utilizing the parameters gathered in initial ED evaluations has acceptable performance for the prediction of in-hospital mortality. Standardized W statistics based on this scoring system can be used to compare the performance of an ED with the reference data or with the performance of other institutions.

scholarly journals Development and Validation of a Prognostic Nomogram to Predict Overall Survival of Elderly Patients with Gastric Cancer

2020 ◽  
Author(s):  
Xingguo Zhou ◽  
Yinlu Ding ◽  
Yu Wang ◽  
Ying Xue ◽  
Yifeng Zang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Elderly Patients ◽  
Receiver Operating Characteristic ◽  
Clinical Utility ◽  
Operating Characteristic ◽  
Term Survival ◽  
Ajcc Stage ◽  
Development And Validation ◽  
Receiver Operating

Abstract Background Gastric cancer (GC) is one of the most common malignant tumors of digestive tract origin in China. The proportion of elderly patients with gastric cancer (GC) gradually increases as the population ages. We aimed to develop a prognostic nomogram for prediction of elderly (≥ 75 years old) GC patients in overall survival (OS). Patients and Methods Patients with GC from 2005 to 2014 were selected from the Surveillance, Epidemiology, and End Result (SEER) database and randomly assigned to development and validation sets. The variables for establishing nomogram were confirmed by univariate and multivariate Cox proportional hazard analysis based on the development set. The predictive accuracy and discriminative ability of the model was evaluated using the receiver operating characteristic (ROC) curve, the concordance index (C-index) and calibration curves, while its clinical utility was assessed using decision curve analysis (DCA) and Kaplan-Meier curve. Results A total of 1445 patients were included in this study. The nomogram was developed including histologic grade, AJCC stage T, N, M and surgery according to the univariate and multivariate cox regression analysis, the area under the time-dependent receiver operating characteristic curve (AUC) and Occam’s Law of Razor. The C-index of the nomogram was higher than the TNM system in the training cohort (0.710 vs 0.652, p < 0.001), which was also confirmed in the validation cohort (0.701 vs 0.643, p < 0.001); and high AUCs were noted in both development and validation sets. The nomogram showed good discrimination and calibration in both development and validation sets. The DCA curves showed that the nomogram had better clinical utility compared to the AJCC stage model. In addition, participants could be divided into three disparate risk groups (low, moderate, high) by the nomogram. Conclusion This study established a prognostic nomogram that improved the performance of the AJCC staging system with incorporation of risk factors to better predict the short-term survival in elderly GC patients.

scholarly journals Flexion Dysfunction of Atlanto-occipital Joint as a Risk Factor for Cervical Spondylosis

2020 ◽  
Author(s):  
Gong Long ◽  
Yi Ping ◽  
Tan Mingsheng
Keyword(s):  
Risk Factor ◽  
Receiver Operating Characteristic ◽  
Cervical Spondylosis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Neck Disability Index ◽  
Curve Analysis ◽  
Control Group ◽  
Increased Risk ◽  
Receiver Operating

Abstract BACKGROUND: There exist varied craniocervical flexion angles from the teenagers to the elderly. To our best knowledge, there is no prior study to examine the role of range of motion (ROM) of the atlanto-occipital joint in the pathogenesis of cervical spondylosis (CS). The purpose of this study was to investigate the association between atlanto-occipital radiographic alignment in flexion and CS.METHODS: 232 CS patients, including 45 patients who accepted surgical treatment, were retrospectively reviewed. The angle between McGregor’s line and C1 line (O-C1 angle) was evaluated on images taken in flexion (F-OC) and neutral positions (N-OC) independently. The relationship between the FOC (FOC=F-OC—N-OC) and Neck Disability Index (NDI) was examined, and the involvement of the FOC in the onset of CS was analyzed. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off for detecting an increased risk of CS.RESULTS: The FOC showed a significant correlation with NDI(P<0.05). The mean FOC was significantly lower in the CS groups than in the control group (P<0.001). Logistic regression analysis showed involvement of the FOC in the onset of CS, and the threshold value according to receiver operating characteristic curve analysis was 4.2 degree, with the odds ratio of 8.2 (95% CI:6.4–10.0; P<0.001). CONCLUSION: Stiff atlanto-occipital joint, represented by low FOC, is an independent risk factor in the incidence of CS compared with healthy individuals. This parameter can help spine surgeons to identify these people to implement appropriate preventive and management steps.

scholarly journals Acute Myocardial Infarction During Hospitalization for Community-Acquired Pneumonia in Geriatric Patients: A Cross-Sectional Study

2020 ◽  
Author(s):  
Yu Kang ◽  
Xiang-Yang Fang ◽  
Dong Wang ◽  
Xiao-Juan Wang
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Respiratory Failure ◽  
Elderly Patients ◽  
Hospital Mortality ◽  
Geriatric Patients ◽  
The Elderly ◽  
Community Acquired Pneumonia ◽  
Cross Sectional

Abstract Background: Community-acquired pneumonia (CAP) and acute myocardial infarction cardiovascular (AMI) are two important health issues in elderly. Little is known regarding characteristics of AMI in elderly hospitalized for CAP. Therefore, we investigated the prevalence, characteristics compared with younger patients, impact on clinical outcomes and risk factors of AMI during hospitalization for CAP in geriatric patients.Methods: 11009 adult inpatients consisted of 5111 elderly patients≥ 65 years and 5898 patients＜65 years in respiratory ward and 1095 inpatients ≥65 years in geriatrics ward diagnosed with CAP were retrospectively analyzed by electronic medical records. Results: 159 (3.1%) elderly patients in respiratory ward and 77 (7.0%) patients in geriatrics ward experienced AMI during hospitalization for CAP. AMI were more frequently seen in elderly patients (3.1% vs. 1.0 %), Patients≥65 years who experienced AMI during hospitalization for CAP had higher percentage of respiratory failure (P = 0.001), hypertension (P = 0.008), dyspnea (P=0.046), blood urea nitrogen (BUN)≥7mmol/L (P < 0.001), serum sodium <130 mmol/L(P = 0.005) and had higher in-hospital mortality compared to patients＜65 years (10.1% vs. 6.6%). AMI was associated with increased in-hospital mortality (odds ratio, OR, with 95% confidence interval: 1.49 [1.24-1.82]; P<0.01). Respiratory failure (OR, 1.34 [1.15–1.54]; P<0.01), preexisting coronary artery disease (OR, 1.31[1.07–1.59]; P = 0.02), diabetes (OR, 1.26 [1.11–1.42]; P = 0.02), BUN (OR, 1.23 [1.01–1.49]; P = 0.04), and impaired consciousness (OR, 1.19 [1.07–1.32]; P = 0.03) were correlated with the occurrence of AMI in the elderly.Conclusions: The incidence of AMI during CAP hospitalization in geriatric patients is notable and have an impact on in-hospital mortality. Characteristics of the elderly differ from the general population. Particular attention should be paid to elderly patients with risk factors for AMI. Our study may represent useful information for clinical strategies aimed at preventing AMI and decreasing mortality in geriatric patients hospitalization for CAP.

scholarly journals Admission Bedside Lung Ultrasound Reclassifies Mortality Prediction in Patients With ST-Segment–Elevation Myocardial Infarction

2020 ◽  
Vol 13 (6) ◽  
Author(s):  
Gustavo N. Araujo ◽  
Anderson D. Silveira ◽  
Fernando L. Scolari ◽  
Julia L. Custodio ◽  
Felipe P. Marques ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Pulmonary Congestion ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
Net Reclassification Improvement ◽  
St Segment ◽  
Receiver Operating

Background: Early risk stratification is essential for in-hospital management of ST-segment–elevation myocardial infarction. Acute heart failure confers a worse prognosis, and although lung ultrasound (LUS) is recommended as a first-line test to assess pulmonary congestion, it has never been tested in this setting. Our aim was to evaluate the prognostic ability of admission LUS in patients with ST-segment–elevation myocardial infarction. Methods: LUS protocol consisted of 8 scanning zones and was performed before primary percutaneous coronary intervention by an operator blinded to Killip classification. A LUS combined with Killip (LUCK) classification was developed. Receiver operating characteristic and net reclassification improvement analyses were performed to compare LUCK and Killip classifications. Results: We prospectively investigated 215 patients admitted with ST-segment–elevation myocardial infarction between April 2018 and June 2019. Absence of pulmonary congestion detected by LUS implied a negative predictive value for in-hospital mortality of 98.1% (93.1–99.5%). The area under the receiver operating characteristic curve of the LUCK classification for in-hospital mortality was 0.89 ( P =0.001), and of the Killip classification was 0.86 ( P <0.001; P =0.05 for the difference between curves). LUCK classification improved Killip ability to predict in-hospital mortality with a net reclassification improvement of 0.18. Conclusions: In a cohort of patients with ST-segment–elevation myocardial infarction undergoing primary percutaneous coronary intervention, admission LUS added to Killip classification was more sensitive than physical examination to identify patients at risk for in-hospital mortality. LUCK classification had a greater area under the receiver operating characteristic curve and reclassified Killip classification in 18% of cases. Moreover, absence of pulmonary congestion on LUS provided an excellent negative predictive value for in-hospital mortality.

scholarly journals Can machine learning improve mortality prediction following cardiac surgery?

2020 ◽  
Vol 58 (6) ◽  
pp. 1130-1136
Author(s):  
Umberto Benedetto ◽  
Shubhra Sinha ◽  
Matt Lyon ◽  
Arnaldo Dimagli ◽  
Tom R Gaunt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Random Forest ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract OBJECTIVES Interest in the clinical usefulness of machine learning for risk prediction has bloomed recently. Cardiac surgery patients are at high risk of complications and therefore presurgical risk assessment is of crucial relevance. We aimed to compare the performance of machine learning algorithms over traditional logistic regression (LR) model to predict in-hospital mortality following cardiac surgery. METHODS A single-centre data set of prospectively collected information from patients undergoing adult cardiac surgery from 1996 to 2017 was split into 70% training set and 30% testing set. Prediction models were developed using neural network, random forest, naive Bayes and retrained LR based on features included in the EuroSCORE. Discrimination was assessed using area under the receiver operating characteristic curve, and calibration analysis was undertaken using the calibration belt method. Model calibration drift was assessed by comparing Goodness of fit χ2 statistics observed in 2 equal bins from the testing sample ordered by procedure date. RESULTS A total of 28 761 cardiac procedures were performed during the study period. The in-hospital mortality rate was 2.7%. Retrained LR [area under the receiver operating characteristic curve 0.80; 95% confidence interval (CI) 0.77–0.83] and random forest model (0.80; 95% CI 0.76–0.83) showed the best discrimination. All models showed significant miscalibration. Retrained LR proved to have the weakest calibration drift. CONCLUSIONS Our findings do not support the hypothesis that machine learning methods provide advantage over LR model in predicting operative mortality after cardiac surgery.

scholarly journals Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality

2019 ◽  
Author(s):  
Nathan Brajer ◽  
Brian Cozzi ◽  
Michael Gao ◽  
Marshall Nichols ◽  
Mike Revoir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Learning Model ◽  
Adult Patients ◽  
System Level ◽  
Machine Learning Model ◽  
Time Of Admission ◽  
Receiver Operating

AbstractThe ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few machine learning models have been developed to predict in-hospital death that are both broadly applicable to all adult patients across a health system and readily implementable, and, to the best of our knowledge, none have been implemented, evaluated prospectively, or externally validated.The primary objective of this study was to prospectively and externally validate a machine learning model that predicts in-hospital mortality for all adult patients at the time of hospital admission. Model performance was quantified using the area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Secondary objectives were to design the model using commonly available EHR data and accessible computational methods.A total of 75,247 hospital admissions (median [IQR] age 59.5 [29.0] years; male [45.9%]) were included in the study. The in-hospital mortality rates for the training validation, retrospective validations at Hospitals A, B, and C, and prospective validation cohorts, respectively, were 3.0%, 2.7%, 1.8%, 2.1%, and 1.6%. The area under the receiver operating characteristic curves (AUROCs), respectively, were 0.87 (0.83-0.89), 0.85 (0.83-0.87), 0.89 (0.86 – 0.92), 0.84 (0.80-0.89), and 0.86 (0.83-0.90). The area under the precision recall curves (AUPRCs), respectively, were 0.29 (0.25-0.37), 0.17 (0.13-0.22), 0.22 (0.14-0.31), 0.13 (0.08-0.21), and 0.14 (0.09-0.21).The results demonstrated accurate prediction of in-hospital mortality for adult patients at the time of admission. The data elements, methods, and patient selection make the model implementable at a system-level.

Übereinstimmung von klinischer Diagnose, strukturierten Interviews und Selbstbeurteilungsfragebögen bei Depression im Kindes- und Jugendalter

2012 ◽  
Vol 40 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Kathrin Dolle ◽  
Gerd Schulte-Körne ◽  
Nikolaus von Hofacker ◽  
Yonca Izat ◽  
Antje-Kathrin Allgaier
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Kinder Und Jugendliche ◽  
Prädiktive Werte ◽  
Receiver Operating

Fragestellung: Die vorliegende Studie untersucht die Übereinstimmung von strukturierten Kind- und Elterninterviews sowie dem klinischen Urteil bei der Diagnostik depressiver Episoden im Kindes- und Jugendalter. Zudem prüft sie, ob sich die Treffsicherheit und die optimalen Cut-off-Werte von Selbstbeurteilungsfragebögen in Referenz zu diesen verschiedenen Beurteilerperspektiven unterscheiden. Methodik: Mit 81 Kindern (9–12 Jahre) und 88 Jugendlichen (13–16 Jahre), die sich in kinder- und jugendpsychiatrischen Kliniken oder Praxen vorstellten, und ihren Eltern wurden strukturierte Kinder-DIPS-Interviews durchgeführt. Die Kinder füllten das Depressions-Inventar für Kinder und Jugendliche (DIKJ) aus, die Jugendlichen die Allgemeine Depressions-Skala in der Kurzform (ADS-K). Übereinstimmungen wurden mittels Kappa-Koeffizienten ermittelt. Optimale Cut-off-Werte, Sensitivität, Spezifität sowie positive und negative prädiktive Werte wurden anhand von Receiver operating characteristic (ROC) Kurven bestimmt. Ergebnisse: Die Interviews stimmten untereinander sowie mit dem klinischen Urteil niedrig bis mäßig überein. Depressive Episoden wurden häufiger nach klinischem Urteil als in den Interviews festgestellt. Cut-off-Werte und Validitätsmaße der Selbstbeurteilungsfragebögen variierten je nach Referenzstandard mit den schlechtesten Ergebnissen für das klinische Urteil. Schlussfolgerungen: Klinische Beurteiler könnten durch den Einsatz von strukturierten Interviews profitieren. Strategien für den Umgang mit diskrepanten Kind- und Elternangaben sollten empirisch geprüft und detailliert beschrieben werden.

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