Dataset Information

Investigating hospital heterogeneity with a multi-state frailty model: application to nosocomial pneumonia disease in intensive care units.

ABSTRACT:

Background

Multistate models have become increasingly useful to study the evolution of a patient's state over time in intensive care units ICU (e.g. admission, infections, alive discharge or death in ICU). In addition, in critically-ill patients, data come from different ICUs, and because observations are clustered into groups (or units), the observed outcomes cannot be considered as independent. Thus a flexible multi-state model with random effects is needed to obtain valid outcome estimates.

Methods

We show how a simple multi-state frailty model can be used to study semi-competing risks while fully taking into account the clustering (in ICU) of the data and the longitudinal aspects of the data, including left truncation and right censoring. We suggest the use of independent frailty models or joint frailty models for the analysis of transition intensities. Two distinct models which differ in the definition of time t in the transition functions have been studied: semi-Markov models where the transitions depend on the waiting times and nonhomogenous Markov models where the transitions depend on the time since inclusion in the study. The parameters in the proposed multi-state model may conveniently be computed using a semi-parametric or parametric approach with an existing R package FrailtyPack for frailty models. The likelihood cross-validation criterion is proposed to guide the choice of a better fitting model.

Results

We illustrate the use of our approach though the analysis of nosocomial infections (ventilator-associated pneumonia infections: VAP) in ICU, with "alive discharge" and "death" in ICU as other endpoints. We show that the analysis of dependent survival data using a multi-state model without frailty terms may underestimate the variance of regression coefficients specific to each group, leading to incorrect inferences. Some factors are wrongly significantly associated based on the model without frailty terms. This result is confirmed by a short simulation study. We also present individual predictions of VAP underlining the usefulness of dynamic prognostic tools that can take into account the clustering of observations.

Conclusions

The use of multistate frailty models allows the analysis of very complex data. Such models could help improve the estimation of the impact of proposed prognostic features on each transition in a multi-centre study. We suggest a method and software that give accurate estimates and enables inference for any parameter or predictive quantity of interest.

SUBMITTER: Liquet B

PROVIDER: S-EPMC3537543 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Publications

Investigating hospital heterogeneity with a multi-state frailty model: application to nosocomial pneumonia disease in intensive care units.

Liquet Benoit B Timsit Jean-François JF Rondeau Virginie V

BMC medical research methodology 20120615

<h4>Background</h4>Multistate models have become increasingly useful to study the evolution of a patient's state over time in intensive care units ICU (e.g. admission, infections, alive discharge or death in ICU). In addition, in critically-ill patients, data come from different ICUs, and because observations are clustered into groups (or units), the observed outcomes cannot be considered as independent. Thus a flexible multi-state model with random effects is needed to obtain valid outcome esti ...[more]

PMID: 22702430

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Project description:BackgroundIt is essential to determine the distribution of the causative microorganisms in the region and the status of local antibiotic resistance for the proper treatment of hospital-acquired pneumonia/ventilator-associated pneumonia (HAP/VAP). This study aimed to investigate the occurrence and causative strains of HAP/VAP, distribution of resistant bacteria, use of antibiotics, and the ensuing outcomes of patients in Korea.MethodsA multicenter prospective observational cohort study was conducted among patients with HAP/VAP admitted to the medical intensive care unit of 5 tertiary referral centers between August 2012 and June 2015. Patients' demographic and clinical data were collected.ResultsA total of 381 patients were diagnosed with HAP/VAP. Their median age was 69 (59-76) years and 71% were males. A majority of the patients (88%) had late-onset (> 5 days) HAP/VAP. One-quarter of the patients (n = 99) had at least one risk factor for multidrug-resistant (MDR) pathogens, such as prior intravenous antibiotic use within the last 90 days. Microbiological specimens were mostly obtained noninvasively (87%) using sputum or endotracheal aspirates. Pathogens were identified in 235 (62%) of the 381 patients. The most common bacterial pathogen was Acinetobacter baumannii (n = 89), followed by Staphylococcus aureus (n = 52), Klebsiella pneumoniae (n = 25) and Pseudomonas aeruginosa (n = 22). Most of isolated A. baumannii (97%) and S. aureus (88%) were multidrug resistant. The most commonly used empirical antibiotic regimens were carbapenem-based antibiotics (38%), followed by extended-spectrum penicillin/β-lactamase inhibitor (34%). Glycopeptide or linezolid were also used in combination in 54% of patients. The 28-day mortality rate of the patients with HAP/VAP was 30% and the 60-day mortality was 46%. Patients who used empirical antibiotics appropriately had significantly lower mortality rates than those who did not (28-day mortality: 25% vs. 40%, P = 0.032; 60-day mortality: 41% vs. 55%, P = 0.032, respectively). Administration of appropriate empirical antibiotics (odds ratio [OR], 0.282; confidence interval [CI], 0.092-0.859; P = 0.026), Day 7 treatment failure (OR, 4.515; CI, 1.545-13.192; P = 0.006), and APACHE II score on day 1 (OR, 1.326; CI, 0.988-1.779; P = 0.012) were the factors that determined the 28-day mortality in patients with HAP who had identified bacteria as pathogens.ConclusionIn HAP/VAP patients, there was a large burden of MDR pathogens, and their associated mortality rate was high. Proper selection of empirical antibiotics was significantly associated with the patient's prognosis; however, there was a discrepancy between major pathogens and empirical antibiotic therapy.

Project description:IntroductionThe preventive impact of hospital-acquired infection (HAI) surveillance is difficult to assess. Our objective was to investigate the effect of HAI surveillance disruption on ventilator-associated pneumonia (VAP) incidence.MethodsA quasi-experimental study with an intervention group and a control group was conducted between 1 January 2004 and 31 December 2010 in two intensive care units (ICUs) of a university hospital that participated in a national HAI surveillance network. Surveillance was interrupted during the year 2007 in unit A (intervention group) and was continuous in unit B (control group). Period 1 (pre-test period) comprised patients hospitalized during 2004 to 2006, and period 2 (post-test period) involved patients hospitalized during 2008 to 2010. Patients hospitalized ? 48 hours and intubated during their stay were included. Multivariate Poisson regression was fitted to ascertain the influence of surveillance disruption.ResultsA total of 2,771 patients, accounting for 19,848 intubation-days at risk, were studied; 307 had VAP. The VAP attack rate increased in unit A from 7.8% during period 1 to 17.1% during period 2 (P <0.001); in unit B, it was 7.2% and 11.2% for the two periods respectively (P = 0.17). Adjusted VAP incidence rose in unit A after surveillance disruption (incidence rate ratio = 2.17, 95% confidence interval 1.05 to 4.47, P = 0.036), independently of VAP trend; no change was observed in unit B. All-cause mortality and length of stay increased (P = 0.028 and P = 0.038, respectively) in unit A between periods 1 and 2. In unit B, no change in mortality was observed (P = 0.22), while length of stay decreased between periods 1 and 2 (P = 0.002).ConclusionsVAP incidence, length of stay and all-cause mortality rose after HAI surveillance disruption in ICU, which suggests a specific effect of HAI surveillance on VAP prevention and reinforces the role of data feedback and counselling as a mechanism to facilitate performance improvement.

Project description:BackgroundDespite the high mortality in patients with pneumonia admitted to an ICU, data on risk factors for death remain limited.MethodsIn this secondary analysis of PROTECT (Prophylaxis for Thromboembolism in Critical Care Trial), we focused on the patients admitted to ICU with a primary diagnosis of pneumonia. The primary outcome for this study was 90-day hospital mortality and the secondary outcome was 90-day ICU mortality. Cox regression model was conducted to examine the relationship between baseline and time-dependent variables and hospital and ICU mortality.ResultsSix hundred sixty seven patients admitted with pneumonia (43.8 % females) were included in our analysis, with a mean age of 60.7 years and mean APACHE II score of 21.3. During follow-up, 111 patients (16.6 %) died in ICU and in total, 149 (22.3 %) died in hospital. Multivariable analysis demonstrated significant independent risk factors for hospital mortality including male sex (hazard ratio (HR) = 1.5, 95 % confidence interval (CI): 1.1 - 2.2, p-value = 0.021), higher APACHE II score (HR = 1.2, 95 % CI: 1.1 - 1.4, p-value < 0.001 for per-5 point increase), chronic heart failure (HR = 2.9, 95 % CI: 1.6 - 5.4, p-value = 0.001), and dialysis (time-dependent effect: HR = 2.7, 95 % CI: 1.3 - 5.7, p-value = 0.008). Higher APACHE II score (HR = 1.2, 95 % CI: 1.1 - 1.4, p-value = 0.002 for per-5 point increase) and chronic heart failure (HR = 2.6, 95 % CI: 1.3 - 5.0, p-value = 0.004) were significantly related to risk of death in the ICU.ConclusionIn this study using data from a multicenter thromboprophylaxis trial, we found that male sex, higher APACHE II score on admission, chronic heart failure, and dialysis were independently associated with risk of hospital mortality in patients admitted to ICU with pneumonia. While high illness severity score, presence of a serious comorbidity (heart failure) and need for an advanced life support (dialysis) are not unexpected risk factors of mortality, male sex might necessitate further exploration. More studies are warranted to clarify the effect of these risk factors on survival in critically ill patients admitted to ICU with pneumonia.Trial registrationClinicalTrials.gov Identifier: NCT00182143 .

Dataset Information

Investigating hospital heterogeneity with a multi-state frailty model: application to nosocomial pneumonia disease in intensive care units.

Background

Methods

Results

Conclusions

Publications

Investigating hospital heterogeneity with a multi-state frailty model: application to nosocomial pneumonia disease in intensive care units.

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