Landmark prediction of nosocomial infection risk to disentangle short- and long-stay patients

The effect of surgical time on perioperative complications in adolescent idiopathic scoliosis cases. A propensity score analysis

BACKGROUND

Posterior spinal instrumentation and fusion (PSIF) for adolescent idiopathic scoliosis (AIS) can be lengthy and complication-ridden. The aim of this study was to evaluate the effect of surgical time on perioperative complications in this procedure when controlling for confounding variables with propensity score analysis.

METHODS

This was an IRB-approved review of electronic health records from 2010 to 2019 at a single tertiary care children's hospital. Patients undergoing PSIF were grouped into "short" (< 6 h) or "long" (≥ 6 h) surgical time groups. Outcome measures were estimated blood loss (EBL), cell saver transfusions, packed red blood cell (pRBC) transfusions, length of stay (LOS), intraoperative monitoring (IOM) alerts, hematocrit, ICU transfer, neurologic loss, surgical site infection, and 90-day readmissions. We controlled for age, sex, BMI, curve severity, number of segments fused, and surgeon factors.

RESULTS

After propensity score matching there were 113 patients in each group. The short surgical time group had lower EBL (median 715, IQR 550-900 vs median 875, IQR 650-1100 cc; p < 0.001), received less cell saver blood (median 120, IQR 60-168 vs median 160, IQR 97-225 cc; p = 0.001), received less intraoperative pRBCs (median 0, IQR 0-0 vs median 0, IQR 0-320, p = 0.002), had shorter average LOS (4.8 ± 1.7 vs 5.4 ± 2.5 days; p = 0.039), and fewer IOM alerts (4.3% vs 18%, p = 0.003).

CONCLUSIONS

Patients with shorter surgical times had less blood loss, received less transfused blood, had a shorter LOS, and fewer IOM alerts compared to patients with longer surgical times. Surgical times < 6 h may have safety and efficacy advantages over longer times.

LEVEL OF EVIDENCE

III.

REDS study: Retrospective effectiveness study of dalbavancin and other standard of care of the same IV antibiotic class in patients with ABSSSI

OBJECTIVES

Acute bacterial skin and skin-structure infections (ABSSSIs) are a common source of morbidity in both the community and hospital settings. The current standard of care (SoC) requires multiple-dose intravenous (IV) regimens, which are associated with high hospitalisation rates, concomitant event risks and costs. Dalbavancin is a lipoglycopeptide, long-acting antibiotic that is effective against Gram-positive microorganisms, including methicillin-resistant Staphylococcus aureus (MRSA). Dalbavancin allows treatment of ABSSSIs with a single-shot IV administration or once weekly for 2 weeks, enabling clinicians to treat patients in an outpatient setting or to shorten the length of hospital stay.

METHODS

This multicentre, observational, retrospective study compared hospitalised patients who received dalbavancin and patients treated with the three most used IV antibiotics of the same or similar class: vancomycin, teicoplanin and daptomycin. The primary outcome was the time to discharge after starting the study antibiotics.

RESULTS

The primary endpoint, time to discharge from the study therapy start, was measured for both groups: the median number of days was 6.5 in the dalbavancin group vs. 11.0 days in the SoC group. Moreover, in subpopulations of patients receiving one or more concomitant antibiotics active for Gram-positives, MRSA and patients with the most prevalent comorbidity (i.e., diabetes), the advantage of dalbavancin in terms of length of stay was confirmed, with a halved time to discharge or more. Safety data on dalbavancin were consistent with data collected in clinical trials. No serious adverse drug reactions related to dalbavancin were reported and most of them were classified as skin and subcutaneous tissue disorders. One serious ADR was reported for daptomycin.

CONCLUSIONS

Although the analysis was only descriptive, it can be concluded that dalbavancin may enable a remarkable reduction in length of hospital stay, also confirming the clinical effectiveness and good safety profile demonstrated in clinical trials in a real-world setting.

A Discrete Density Approach to Bayesian Quantile and Expectile Regression with Discrete Responses

For decades, regression models beyond the mean for continuous responses have attracted great attention in the literature. These models typically include quantile regression and expectile regression. But there is little research on these regression models for discrete responses, particularly from a Bayesian perspective. By forming the likelihood function based on suitable discrete probability mass functions, this paper introduces a discrete density approach for Bayesian inference of these regression models with discrete responses. Bayesian quantile regression for discrete responses is first developed, and then this method is extended to Bayesian expectile regression for discrete responses. The posterior distribution under this approach is shown not only coherent irrespective of the true distribution of the response, but also proper with regarding to improper priors for the unknown model parameters. The performance of the method is evaluated via extensive Monte Carlo simulation studies and one real data analysis.

Dynamics of nosocomial parainfluenza virus type 3 and influenza virus infections at a large German University Hospital between 2012 and 2019

Nosocomial virus infections cause significant morbidity and mortality. Besides influenza viruses, the disease burden of parainfluenza virus type 3 (PIV-3) is comparatively high among hospitalized patients and severe disease courses can occur. PIV-3 showed the highest rates of nosocomial infections of a panel of respiratory viruses. Therefore, a retrospective observational study was conducted among patients with either PIV-3 or influenza viruses, which served as reference pathogen. The aim was to compare the seasonal dynamics and clinical characteristics of nosocomial infections with these highly transmittable viruses. Nosocomial infection occurred in 15.8% (n = 177) of all influenza cases, mainly in the first half of a season. About 24.3% (n = 104) of the PIV-3 cases were nosocomial and occurred mainly in the second half of a season. Both nosocomial rates of influenza and nosocomial rates of PIV-3 varied between the seasons. Community acquired and nosocomial cases differed in underlying medical conditions and immunosuppression. Knowledge of the baseline rates of nosocomial infections could contribute to the implementation of appropriate infection control measures.

Prolonged Length of Stay and Risk of Unplanned 30-Day Readmission After Elective Spine Surgery: Propensity Score-Matched Analysis of 33,840 Patients

STUDY DESIGN

Retrospective database study.

OBJECTIVE

To assess the association between prolonged length of hospital stay (pLOS) (≥4 d) and unplanned readmission in patients undergoing elective spine surgery by controlling the clinical and statistical confounders.

SUMMARY OF BACKGROUND DATA

pLOS has previously been cited as a risk factor for unplanned hospital readmission. This potentially modifiable risk factor has not been distinguished as an independent risk factor in a large-scale, multi-institutional, risk-adjusted study.

METHODS

Data were collected from the American College of Surgeons National Surgical Quality Improvement Program database. A retrospective propensity score-matched analysis was used to reduce baseline differences between the cohorts. Univariate and multivariate analyses were performed to assess the degree of association between pLOS and unplanned readmission.

RESULTS

From the 99,575 patients that fit the inclusion criteria, propensity score matching yielded 16,920 well-matched pairs (mean standard propensity score difference = 0.017). The overall 30-day unplanned readmission rate of these 33,840 patients was 5.5%. The mean length of stay was 2.0 ± 0.9 days and 6.0 ± 4.5 days (P ≤ 0.001) for the control and pLOS groups, respectively. In our univariate analysis, pLOS was associated with postoperative complications, especially medical complications (22.7% vs. 8.3%, P < 0.001). Multivariate analysis of the propensity score-matched population, which adjusted identified confounders (P < 0.02 and ≥10 occurrences), showed pLOS was associated with an increased risk of 30-day unplanned readmission (odds ratio [OR] 1.423, 95% confidence interval [CI] 1.290-1.570, P < 0.001).

CONCLUSION

Patients who undergo elective spine procedures who have any-cause pLOS (≥4 d) are at greater risk of having unplanned 30-day readmission compared with patients with shorter hospital stays.

LEVEL OF EVIDENCE

4.

Excess mortality due to pandrug-resistant Acinetobacter baumannii infections in hospitalized patients

BACKGROUND

Pandrug-resistant Acinetobacter baumannii (PDRAB) is increasingly being reported as a nosocomial pathogen worldwide, but determining its clinical impact is challenging.

AIM

To assess the spectrum of excess mortality attributable to PDRAB infection in acute care settings.

METHODS

This four-year cohort study was conducted in a tertiary-care referral hospital in Greece to estimate excess in-hospital mortality due to PDRAB infection by comparing patients infected to those colonized with PDRAB by means of competing risks survival analysis.

FINDINGS

The study cohort comprised 91 patients (median age: 67 years; 77% men). For most patients, PDRAB was first isolated in the intensive care unit (ICU) (N = 51; 57%) or following ICU discharge (N = 26; 29%). Overall in-hospital mortality was 68% (95% confidence interval (CI): 57.5-77.5%). PDRAB-infected patients (N = 62; 68%) and PDRAB-colonized patients (N = 29; 32%) had similar baseline characteristics, but the absolute excess risk of 30-day mortality in infected patients compared to colonized patients was 34% (95% CI: 14-54%). Multivariable competing risks regression showed that PDRAB infection significantly increased the daily hazard of 30-day in-hospital death (cause-specific hazard ratio (csHR): 3.10; 95% CI: 1.33-7.21) while simultaneously decreasing the daily rate of discharge (csHR: 0.24; 95% CI: 0.08-0.74), thereby leading to longer hospitalization. Stronger effects were observed for bloodstream infections.

CONCLUSION

New effective antimicrobials would be expected to prevent mortality in one of every three patients treated for PDRAB infection and reduce their length of hospitalization. However, available therapeutic options remain extremely limited and emphasis on preventing healthcare-associated transmission of PDRAB is ever more important.

What can be learned from crude intensive care unit mortality? Methodological implications

PURPOSE

Demonstrate the practical range of information that can be obtained about ICU mortality/survival from limited administrative data.

MATERIALS AND METHODS

Prospectively collected administrative data (length-of stay, survival/mortality, referring service) from a university medical center's General ICU was subjected to retrospective analysis to demonstrate ways of presenting and analyzing mortality/survival information.

RESULTS

16,022 patients (87,624 patient-days) admitted over 23 years were included. 28% of all deaths occurred on ICU day 1. When considering all admissions, mortality on ICU day 1 was 2%, while the overall crude mortality rate revealed that the chances of dying during an ICU stay was 8.6%. Mortality rates in the overall population steadily increased over ICU days 1-5, plateaued during days 6 to 50, decreasing after day 50. The general surgery subgroup had a similar pattern. This contrasted with the internal medicine subgroup where mortality steadily increased over the initial 14 ICU days then plateauing at rates of 40-50%.

INTERPRETATION

Simple calculations using the few variables found in administrative database enhanced information provided by the crude mortality rate and demonstrated that temporal patterns of mortality change as stay lengthens. These results highlight the limitations of just using overall crude mortality rates.

Organisational and financial consequences of the early discharge of patients treated for acute bacterial skin and skin structure infection and osteomyelitis in infectious disease departments in Greece, Italy and Spain: a scenario analysis

OBJECTIVE

The aim of the analysis is to assess the organisational and economic consequences of adopting an early discharge strategy for the treatment of acute bacterial skin and skin structure infection (ABSSSI) and osteomyelitis within infectious disease departments.

SETTING

Infectious disease departments in Greece, Italy and Spain.

PARTICIPANTS

No patients were involved in the analysis performed.

INTERVENTIONS

An analytic framework was developed to consider two alternative scenarios: standard hospitalisation care or an early discharge strategy for patients hospitalised due to ABSSSI and osteomyelitis, from the perspective of the National Health Services of Greece, Italy and Spain. The variables considered were: the number of annual hospitalisations eligible for early discharge, the antibiotic treatments considered (ie, oral antibiotics and intravenous long-acting antibiotics), diagnosis-related group (DRG) reimbursements, number of days of hospitalisation, incidence and costs of hospital-acquired infections, additional follow-up visits and intravenous administrations. Data were based on published literature and expert opinions.

PRIMARY AND SECONDARY OUTCOME MEASURES

Number of days of hospitalisation avoided and direct medical costs avoided.

RESULTS

The total number of days of hospitalisation avoided on a yearly basis would be between 2216 and 5595 in Greece (-8/-21 hospital beds), between 15 848 and 38 444 in Italy (-57/-135 hospital beds) and between 7529 and 23 520 in Spain (-27/-85 hospital beds). From an economic perspective, the impact of the early discharge scenario is a reduction between €45 036 and €149 552 in Greece, a reduction between €182 132 and €437 990 in Italy and a reduction between €292 284 and €884 035 in Spain.

CONCLUSIONS

The early discharge strategy presented would have a positive organisational impact on National Health Services, leading to potential savings in beds, and to a reduction of hospital-acquired infections and costs.

The population-attributable fraction for time-dependent exposures and competing risks-A discussion on estimands

The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance, an estimand accommodating complicated time-to-event data is not clearly defined. We discuss current estimands of the PAF used to quantify the public health impact of an internal time-dependent exposure for data subject to competing outcomes. To overcome some limitations, we proposed a novel estimand that is based on dynamic prediction by landmarking. In a profound simulation study, we discuss interpretation and performance of the various estimands and their estimators. The methods are applied to a large French database to estimate the health impact of ventilator-associated pneumonia for patients in intensive care.

Dynamic prediction: A challenge for biostatisticians, but greatly needed by patients, physicians and the public

Prognosis is usually expressed in terms of the probability that a patient will or will not have experienced an event of interest t years after diagnosis of a disease. This quantity, however, is of little informative value for a patient who is still event-free after a number of years. Such a patient would be much more interested in the conditional probability of being event-free in the upcoming t years, given that he/she did not experience the event in the s years after diagnosis, called "conditional survival." It is the simplest form of a dynamic prediction and can be dealt with using straightforward extensions of standard time-to-event analyses in clinical cohort studies. For a healthy individual, a related problem with further complications is the so-called "age-conditional probability of developing cancer" in the next t years. Here, the competing risk of dying from other diseases has to be taken into account. For both situations, the hazard function provides the central dynamic concept, which can be further extended in a natural way to build dynamic prediction models that incorporate both baseline and time-dependent characteristics. Such models are able to exploit the most current information accumulating over time in order to accurately predict the further course or development of a disease. In this article, the biostatistical challenges as well as the relevance and importance of dynamic prediction are illustrated using studies of multiple myeloma, a hematologic malignancy with a formerly rather poor prognosis which has improved over the last few years.

Analyzing the impact of duration of ventilation, hospitalization, and ventilation episodes on the risk of pneumonia

Objective:

To study the impact of duration of mechanical ventilation, hospitalization and multiple ventilation episodes on the development of pneumonia while accounting for extubation as a competing event.

Design:

A multicenter data base from a Spanish surveillance network was used to conduct a retrospective analysis of prospectively collected intensive care patients followed from admission to discharge.

Setting:

Spanish intensive care units (ICUs).

Patients:

Mechanically ventilated adult patients from 158 ICUs with 45,486 admissions, 48,705 ventilation episodes, and 314,196 ventilator days.

Methods:

Competing-risk models were applied to account for extubation plus 48 hours as a competing event for acquiring ventilator-associated pneumonia (VAP).

Results:

Time in the ICU before mechanical ventilation was associated with an increased VAP hazard rate and with longer intubation time. This indirect prolongation of intubation increased the cumulative risk to eventually acquire VAP. For instance, comparing 3–4 versus 0 days, the adjusted VAP hazard ratio was 1.40 (95% confidence interval [CI], 1.19–1.64) and the adjusted extubation hazard ratio was 0.64 (95% CI, 0.61–0.68), which leads to an adjusted VAP subdistribution hazard ratio (sHR) of 2.13 (95% CI, 1.83–2.50). Similarly, due to prolonged intubation, multiple ventilation episodes increase the risk for VAP; the adjusted sHR is 1.52 (95% CI, 1.35–1.72) for the second episode compared to the first episode, and the adjusted sHR is 1.54 (95% CI, 1.03–2.30) for the third episode compared to the first episode. The Kaplan-Meier method produced an upward biased estimated cumulative risk for VAP.

Conclusions:

A competing-risk analysis is necessary to receive unbiased risk estimates and to quantify the indirect effect of intubation time on the cumulative VAP risk. Our findings may guide physicians to improve medical decisions related to the harms and benefits of the duration of ventilation.

Improving Prediction Accuracy of "Central Line-Associated Blood Stream Infections" Using Data Mining Models

Prediction of nosocomial infections among patients is an important part of clinical surveillance programs to enable the related personnel to take preventive actions in advance. Designing a clinical surveillance program with capability of predicting nosocomial infections is a challenging task due to several reasons, including high dimensionality of medical data, heterogenous data representation, and special knowledge required to extract patterns for prediction. In this paper, we present details of six data mining methods implemented using cross industry standard process for data mining to predict central line-associated blood stream infections. For our study, we selected datasets of healthcare-associated infections from US National Healthcare Safety Network and consumer survey data from Hospital Consumer Assessment of Healthcare Providers and Systems. Our experiments show that central line-associated blood stream infections (CLABSIs) can be successfully predicted using AdaBoost method with an accuracy up to 89.7%. This will help in implementing effective clinical surveillance programs for infection control, as well as improving the accuracy detection of CLABSIs. Also, this reduces patients' hospital stay cost and maintains patients' safety.

Multistate Modeling to Analyze Nosocomial Infection Data: An Introduction and Demonstration

OBJECTIVE Multistate and competing risks models have become an established and adequate tool with which to quantify determinants and consequences of nosocomial infections. In this tutorial article, we explain and demonstrate the basics of these models to a broader audience of professionals in health care, infection control, and hospital epidemiology. METHODS Using a publicly available data set from a cohort study of intensive care unit patients, we show how hospital infection data can be displayed and explored graphically and how simple formulas are derived under some simplified assumptions for illustrating the basic ideas behind multistate models. Only a few simply accessible values (event counts and patient days) and a pocket calculator are needed to reveal basic insights into cumulative risk and clinical outcomes of nosocomial infection in terms of mortality and length of stay. RESULTS We show how to use these values to perform basic multistate analyses in own data or to correct biased estimates in published data, as these values are often reported. We also show relationships between multistate-based hazard ratios and odds ratios, which are derived from the popular logistic regression model. CONCLUSIONS No sophisticated statistical software is required to apply a basic multistate model and to avoid typical pitfalls such as time-dependent or competing-risks bias. Infect Control Hosp Epidemiol 2017;38:953-959.