Bronchiectasis severity assessment on predicting hospital readmission: a single-center prospective cohort study.
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ABSTRACT:
SUBMITTER: Wang XY
PROVIDER: S-EPMC7909381 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
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Bronchiectasis severity assessment on predicting hospital readmission: a single-center prospective cohort study.
Wang Xiao-Yue XY Li Ru R Wang Wan-Ying WY Li De-Shuai DS Zhou Yu-Yan YY Chen Xiao-Ting XT Bu Xiao-Ning XN
Chinese medical journal 20200901 4
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Project description:BackgroundThe characteristics of bronchiectasis (BE) in Asia, including Japan, remain largely unknown. We aimed to provide insights into the clinical characteristics and treatment outcomes of BE, especially regarding nontuberculous mycobacteria (NTM) infection and its poorly understood impact on prognosis. We also aimed to clarify the effect of long-term macrolide antibiotic use in patients with BE, who had no history of exacerbations.MethodsIn this single-center, retrospective study, the medical records of patients who satisfied the BE criteria between January 1, 2012, and August 31, 2023, were reviewed. Severe exacerbations and mortality during the observation period were recorded. Baseline characteristics and overall survival of patients with and without NTM infection, and factors influencing the time to the first exacerbation and death were analyzed. Additionally, the effects of long-term macrolide antibiotic use in patients without a history of severe exacerbations were estimated.ResultsIn a cohort of 1044 patients with BE, the rate of severe exacerbation was 22.3%, with mortality rates of 3.2% over 3 years. Notably, the high prevalence of NTM infection (n = 410, 39.3%) in this cohort was distinctive. NTM infection was not associated with either the time to first severe exacerbation (p = 0.5676, adjusted hazard ratio = 1.11) or mortality (p = 0.4139, adjusted hazard ratio = 0.78). Compared with the NTM group, the non-NTM group had a higher proportion of elevated inflammatory markers, with significant differences in C-reactive protein levels (p = 0.0301) and blood neutrophil counts (p = 0.0273). Pseudomonas aeruginosa colonization was more frequent in the non-NTM group (p = 0.0003). Among patients with non-NTM infection and without a history of exacerbation in the past 2 years, 38.2% received long-term macrolide antibiotics that did not invariably prolong the time to first severe exacerbation (p = 0.4517, IPW p = 0.3555).ConclusionsThis study highlights BE epidemiology in Japan, noting that the presence of NTM infection may not necessarily worsen the prognostic outcomes and advising caution in the casual use of macrolides for milder cases without a history of exacerbations.Clinical trial registrationUMIN Clinical Trials Registry Number: UMIN000054726 (Registered on 21 June 2024).
| S-EPMC11520111 | biostudies-literature
Project description:PurposeThis study aims to determine if the primary care provider (PCP) assessment of readmission risk is comparable to the validated LACE tool at predicting readmission to hospital.MethodsA prospective observational study of recently discharged adult patients clustered by PCPs in the primary care setting. Physician readmission risk assessment was determined via a questionnaire after the PCP reviewed the hospital discharge summary. LACE scores were calculated using administrative data and the discharge summary. The sensitivity and specificity of the physician assessment and the LACE tool in predicting readmission risk, agreement between the 2 assessments and the area under receiver operating characteristic (AUROC) curves were calculated.Results217 patient readmission encounters were included in this study from September 2017 till June 2018. The rate of readmission within 30 days was 14.7%, and 217 discharge summaries were used for analysis. The weighted kappa coefficient was 0.41 (95% CI: 0.30-0.51) demonstrating a moderate level of agreement. Sensitivity of physician assessment was 0.31 (95% CI: 0.22-0.40) and specificity was 0.80 (95% CI: 0.77-0.83). The sensitivity of the LACE assessment was 0.42 (95% CI: 0.25-0.59) and specificity was 0.79 (95% CI: 0.73-0.85). The AUROC for the LACE readmission risk was 0.65 (95% C.I. 0.55-0.76) demonstrating modest predictive power and was 0.57 (95% C.I. 0.46-0.68) for physician assessment, demonstrating low predictive power.ConclusionThe LACE index shows moderate discriminatory power in identifying high-risk patients for readmission when compared to the PCP's assessment. If this score can be provided to the PCP, it may help identify patients who requires more intensive follow-up after discharge.
| S-EPMC8673665 | biostudies-literature
Project description:BackgroundEarly hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance.AimsWe leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors.MethodsA national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission.ResultsWe analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration.ConclusionOur model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination.
| S-EPMC7073276 | biostudies-literature
Project description:BackgroundBronchiectasis is a chronic respiratory disease that leads to airway inflammation, destruction, and airflow limitation, which reflects its severity. Impulse oscillometry (IOS) is a non-invasive method that uses sound waves to estimate lung function and airway resistance. The aim of this study was to assess the usefulness of IOS in predicting the severity of bronchiectasis.MethodsWe retrospectively reviewed the IOS parameters and clinical characteristics in 145 patients diagnosed with bronchiectasis between March 2020 and May 2021. Disease severity was evaluated using the FACED score, and patients were divided into mild and moderate/severe groups.ResultsForty-four patients (30.3%) were in the moderate/severe group, and 101 (69.7%) were in the mild group. Patients with moderate/severe bronchiectasis had a higher airway resistance at 5 Hz (R5), a higher difference between the resistance at 5 and 20 Hz (R5-R20), a higher resonant frequency (Fres), and a higher area of reactance (AX) than patients with mild bronchiectasis. R5 ≥0.43, resistance at 20 Hz (R20) ≥0.234, R5-R20 ≥28.3, AX ≥1.02, reactance at 5 Hz (X5) ≤-0.238, and Fres ≥20.88 revealed significant univariable relationships with bronchiectasis severity (p<0.05). Among these, only X5 ≤-0.238 exhibited a significant multivariable relationship with bronchiectasis severity (p=0.039). The receiver operating characteristic curve for predicting moderate- to-severe bronchiectasis of FACED score based on IOS parameters exhibited an area under the curve of 0.809.ConclusionThe IOS assessed by the disease severity of FACED score can effectively reflect airway resistance and elasticity in bronchiectasis patients and serve as valuable tools for predicting bronchiectasis severity.
| S-EPMC11222088 | biostudies-literature
Project description:BackgroundUp to 40% of women gain excessive weight during pregnancy. Obesity complications and risks in parturient women undergoing cesarean section (CS) with different anesthetic methods remain unknown. This study aimed to assess the safety and risk of obese women undergoing CS delivery with various perioperative anesthetic methods.MethodsSeven hundred ninety parturient women underwent CS under general anesthesia (GA), intraspinal anesthesia including epidural anesthesia (EA) and combined spinal-epidural anesthesia (CSEA). They were divided into morbid (n = 255), severe (n = 274), and non-obesity (n = 261) groups. This study is registered with ClinicalTrials.gov (number NCT03002636).ResultBetween 2013 and 2016, 790 pregnant were assessed. Compared with the non-obesity group, there were significantly more fetal distress and higher body mass index (BMI) in the morbid obesity group (P = .0001 and P = .001, respectively). Significantly more patients showed preeclampsia, multifetation, amniotic fluid abnormality, and high bleeding amounts in the morbid obesity group compared with the non-obesity group (P = .0001, P = .048, P = .017, and P = .018, respectively); more patients were administered EA and GA compared with the non-obesity group (P = .0001 and P = .0001, respectively). More post-anesthesia care unit (PACU) patients were found in the severe obesity group no more than the non-obesity group. Significantly increased anesthesia puncture times for 5 > n ≥ 3 and n ≥ 5 were obtained in the morbid obesity group (P = .0001 and P = .0001, respectively), with more patients in the puncture sitting position, compared with the non-obesity group (P = .0001).ConclusionGA, EA, and CSEA are safe and effective in severely or morbidly obese patients. Morbidly obese parturient show increased likelihood for fetal distress, PACU, sitting position puncture, puncture difficulty, and other pregnancy complications. There were more anesthesia puncture times in morbidly obese patients.
| S-EPMC5662385 | biostudies-literature
Project description:Introduction Head and neck surgery remains a complex field; the patients can suffer important functional or life-threating complications after treatment that need unplanned readmissions, increasing the cost related to the treatment. Objective To evaluate the incidence risk factors and causes associated with 30-day unplanned hospital readmission and visit to the emergency room (ER) after surgery for head and neck cancer. Methods Prospective, longitudinal, nonrandomized study. Results A total of 834 patients were included, 726 in the major surgery group and 108 in the minor surgery group. The 30-day readmission rate for all causes was of 7,9% for the patients treated by a major surgery and of 0% for the patients treated in the outpatient clinic for minor procedures, to a total readmission rate of 6,8%. The rate of visit to the emergency room for all causes in the first 30 days was of 14% for the patients treated by a major surgery and of 2,7% for the patients treated in the outpatient clinic. Conclusion Major surgery, the American Society of Anesthesiologists (ASA) status and type of wound are conditions related to unplanned readmission or visit to the ER in the first 30 day after discharge. The most commonly associated causes are infections or wound complications. An evidence-based risk stratification of the patients can be important to improve decision-making and resource utilization. An educational strategy can provide possible ways to improve the rate of readmission and reduce the amount of money expended by healthcare systems.
| S-EPMC8789501 | biostudies-literature
Project description:BackgroundIn December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.MethodsOur study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.ResultsThe receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome.ConclusionOur study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.
| S-EPMC10250627 | biostudies-literature
Project description:ObjectiveWe aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalisation/death, in patients with COVID-19 previously discharged from the ED. We also developed a disease progression velocity index.Design and settingRetrospective cohort study of prospectively collected data. The charts of consecutive patients with COVID-19 discharged from the Reggio Emilia (Italy) ED (2 March 2 to 31 March 2020) were retrospectively examined. Clinical, laboratory and CT findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index.Participants450 patients discharged from the ED with diagnosis of COVID-19.Main outcome measureED readmission within 14 days, followed by hospitalisation/death.ResultsOf the discharged patients, 84 (18.7%) were readmitted to the ED, 61 (13.6%) were hospitalised and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03 to 1.08), Charlson Comorbidity Index 3 versus 0 (OR=11.61; 95% CI 1.76 to 76.58), days from symptom onset (OR for 1-day increase=0.81; 95% CI 0.73 to 0.90) and CT extension (OR for 1% increase=1.03; 95% CI 1.01 to 1.06) were associated in a multivariable model for readmission with hospitalisation/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08 to 1.36); the model including this index resulted in less information loss.ConclusionsA velocity index combining CT extension and days from symptom onset predicts disease progression in patients with COVID-19. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.
| S-EPMC8987209 | biostudies-literature
Project description:Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.
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Project description:Virtually, all studies reporting the outcomes of living kidney donation beyond the first year from donation were retrospective. In this prospective study, the outcome of 81 consecutive living kidney donors was thoroughly evaluated. Clinical, laboratory, and radiological assessments were carried out at predonation (basal), 3, 6, 12, and 24 months after donation. The mean age at time of donation was 37.8 ± 9.8 years and the majority was females (75.3%). The mean BMI increased significantly after donation (P < 0.04). The mean serum creatinine levels (mg/dl) were 0.75 ± 0.14, 1.01 ± 0.22, 0.99 ± 0.21, 0.98 ± 0.20, and 0.94 ± 0.20 (P < 0.0001). Likewise, the mean levels of measured creatinine clearance (mL/min) were 148.8 ± 35.7, 94.7 ± 26.6, 95.5 ± 24.6, 96.7 ± 20.2, and 101.6 ± 26.2 (P < 0.0001). The mean 24 hours urinary protein excretion (gm/dL) were 0.09 ± 0.03, 0.19 ± 0.18, 0.16 ± 0.09, 0.18 ± 0.25, and 0.17 ± 0.12 (P < 0.0001). There were significant increases in the means of the longitudinal and transverse diameters of the remaining kidney over time (P < 0.001). Out of 42 female donors, eleven female donors have got successful postdonation pregnancies. There were no reported surgical complications, either intra- or postoperative. Long-term follow-up is necessary for all living kidney donors through local institutional and world registries. This trial is registered with ClinicalTrials.gov NCT00813579.
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