scholarly journals Ability of the LACE index to predict 30-day hospital readmissions in patients with community-acquired pneumonia

2020 ◽  
Vol 6 (2) ◽  
pp. 00301-2019
Author(s):  
Claudia C. Dobler ◽  
Maryam Hakim ◽  
Sidhartha Singh ◽  
Matthew Jennings ◽  
Grant Waterer ◽  
...  
Keyword(s):  
Prediction Models ◽  
Hospital Readmissions ◽  
Predictive Ability ◽  
Tertiary Hospital ◽  
Community Acquired Pneumonia ◽  
Chronic Obstructive ◽  
Day Hospital ◽  
Discriminative Ability ◽  
Obstructive Pulmonary Disease ◽  
C Statistic

Background and objectiveHospital readmissions within 30 days are used as an indicator of quality of hospital care. We aimed to evaluate the ability of the LACE (Length of stay, Acuity of admission, Comorbidities based on Charlson comorbidity score and number of Emergency visits in the last 6 months) index to predict the risk of 30-day readmissions in patients hospitalised for community-acquired pneumonia (CAP).MethodsIn this retrospective cohort study a LACE index score was calculated for patients with a principal diagnosis of CAP admitted to a tertiary hospital in Sydney, Australia. The predictive ability of the LACE score for 30-day readmissions was assessed using receiver operator characteristic curves with C-statistic.ResultsOf 3996 patients admitted to hospital for CAP at least once, 8.0% (n=327) died in hospital and 14.6% (n=584) were readmitted within 30 days. 17.8% (113 of 636) of all 30-day readmissions were again due to CAP, followed by readmissions for chronic obstructive pulmonary disease, heart failure and chest pain. The LACE index had moderate discriminative ability to predict 30-day readmission (C-statistic=0.6395) but performed poorly for the prediction of 30-day readmissions due to CAP (C-statistic=0.5760).ConclusionsThe ability of the LACE index to predict all-cause 30-day hospital readmissions is comparable to more complex pneumonia-specific indices with moderate discrimination. For the prediction of 30-day readmissions due to CAP, the performance of the LACE index and modified risk prediction models using readily available variables (sex, age, specific comorbidities, after-hours, weekend, winter or summer admission) is insufficient.

A review of literature on risk prediction tools for hospital readmissions in older adults

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michelle Louise Gatt ◽  
Maria Cassar ◽  
Sandra C. Buttigieg
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Hospital Readmissions ◽  
Predictive Ability ◽  
Chronic Obstructive ◽  
Risk Category ◽  
Extensive Literature ◽  
Content Type ◽  
Prediction Tools ◽  
Readmission Risk

Purpose The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.Design/methodology/approach Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.Findings Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.Research limitations/implications Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.Originality/value This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

scholarly journals Assessment of Analysis of Urinary Pneumococcal Antigen by Immunochromatography for Etiologic Diagnosis of Community-Acquired Pneumonia in Adults

2006 ◽  
Vol 13 (10) ◽  
pp. 1092-1097 ◽  
Author(s):  
Maria Luisa Briones ◽  
José Blanquer ◽  
David Ferrando ◽  
Maria Luisa Blasco ◽  
Concepción Gimeno ◽  
...  
Keyword(s):  
Tertiary Hospital ◽  
Diagnostic Techniques ◽  
Community Acquired Pneumonia ◽  
Etiologic Agent ◽  
Chronic Obstructive ◽  
Pneumococcal Infections ◽  
Obstructive Pulmonary Disease ◽  
Pneumococcal Antigen ◽  
A Prospective Study ◽  
Etiologic Diagnosis

ABSTRACT The limitations of conventional microbiologic methods (CMM) for etiologic diagnosis of community pneumococcal pneumonia have made faster diagnostic techniques necessary. Our aim was to evaluate the usefulness of the immunochromatography (ICT) technique for detecting urinary Streptococcus pneumoniae antigen in the etiologic diagnosis of community-acquired pneumonias (CAP). This was a prospective study on in-patients with CAP in a tertiary hospital conducted from October 2000 to March 2004. Apart from using CMM to reach an etiologic diagnosis, we determined pneumococcal antigen in concentrated urine by ICT. We also determined the urinary pneumococcal antigen (UPA) content in patients from two control groups to calculate the specificity of the technique. One group was comprised of in-patients diagnosed with chronic obstructive pulmonary disease (COPD) or asthma, with respiratory infection, and without pneumonia; the other group included fractures. We studied 959 pneumonia patients and determined UPA content in 911 (95%) of them. We diagnosed the etiology of 253 cases (28%) using CMM; S. pneumoniae was the most common etiologic agent (57 cases). ICT analysis was positive for 279 patients (31%). Using this technique, the percentage of diagnoses of pneumococcal pneumonias increased by 26%, while the overall etiologic diagnosis increased from 28 to 49%. The technique sensitivity was 81%; the specificity oscillated between 80% in CAP with nonpneumococcal etiology and 99% for patients with fractures without infections. Determination of UPA is a rapid, simple analysis with good sensitivity and specificity, which increased the percentage of etiologic diagnoses. Positive UPA may persist in COPD patients with probable pneumococcal colonization or recent pneumococcal infections.

scholarly journals Influenza in Malaysian adult patients hospitalized with community-acquired pneumonia, acute exacerbation of chronic obstructive pulmonary disease or asthma: a multicenter, active surveillance study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yong Kek Pang ◽  
Ahmad Izuanuddin Ismail ◽  
Yoke Fun Chan ◽  
Adelina Cheong ◽  
Yoong Min Chong ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Acute Exacerbation ◽  
Active Surveillance ◽  
Community Acquired Pneumonia ◽  
Surveillance Study ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
High Dependency ◽  
Increased Risk

Abstract Background Available data on influenza burden across Southeast Asia are largely limited to pediatric populations, with inconsistent findings. Methods We conducted a multicenter, hospital-based active surveillance study of adults in Malaysia with community-acquired pneumonia (CAP), acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and acute exacerbation of asthma (AEBA), who had influenza-like illness ≤10 days before hospitalization. We estimated the rate of laboratory-confirmed influenza and associated complications over 13 months (July 2018–August 2019) and described the distribution of causative influenza strains. We evaluated predictors of laboratory-confirmed influenza and severe clinical outcomes using multivariate analysis. Results Of 1106 included patients, 114 (10.3%) were influenza-positive; most were influenza A (85.1%), with A/H1N1pdm09 being the predominant circulating strain during the study following a shift from A/H3N2 from January–February 2019 onwards. In multivariate analyses, an absence of comorbidities (none versus any comorbidity [OR (95%CI), 0.565 (0.329–0.970)], p = 0.038) and of dyspnea (0.544 (0.341–0.868)], p = 0.011) were associated with increased risk of influenza positivity. Overall, 184/1106 (16.6%) patients were admitted to intensive care or high-dependency units (ICU/HDU) (13.2% were influenza positive) and 26/1106 (2.4%) died (2.6% were influenza positive). Males were more likely to have a severe outcome (ICU/HDU admission or death). Conclusions Influenza was a significant contributor to hospitalizations associated with CAP, AECOPD and AEBA. However, it was not associated with ICU/HDU admission in this population. Study registration, NMRR ID: NMRR-17-889-35,174.

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