The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model–Based Standardized Infection Ratio

Fukuda, Haruhisa; Kuroki, Masahide

doi:10.1017/ice.2015.302

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Finally, our selection of the variables for adjustment on a patient level might have been incomplete. Our selection was based on patient-related risk factors for SSIs in KPRO and HPRO by Fukuda and Kuroki, 25 and it included 2 additional factors, total or partial operation, and reoperation. We might have missed some factors, such as length of hospital stay, medical insurance status and comorbidity, for which adjustment resulted in substantial shifts in hospital rankings in infection-related readmissions in a study by Gohil et al 26 However, adding factors in adjustment would have made the 95% CIs of hospital ranks even larger.…”

Section: Discussionmentioning

confidence: 99%

Interhospital Comparison of Surgical Site Infection Rates in Orthopedic Surgery

Skufca

Ollgren

Virtanen

et al. 2017

Infect. Control Hosp. Epidemiol.

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OBJECTIVE To investigate whether comparison by deep or adjusted deep surgical site infection (SSI) rates in orthopedic surgeries are a better basis for feedback to Finnish hospitals than overall SSI rates DESIGN Retrospective cohort study SETTING Hospitals conducting surveillance of hip arthroplasties (HPROs) and knee arthroplasties (KPROs) in the Finnish Hospital Infection Program METHODS We analyzed surveillance data for 73,227 HPROs and 56,860 KPROs performed in 18 hospitals during 1999-2014. For each hospital, the overall, deep, and adjusted deep SSI rates with 95% confidence intervals (CIs) were calculated, and the hospital ranks were simulated in the Bayesian framework. Adjustments were performed using relevant patient and hospital characteristics. The correlation between the median expected hospital ranks in overall versus deep SSI rates and deep vs adjusted deep SSI rates were assessed using Spearman's correlation coefficient ρ. RESULTS For HPRO, the overall SSI rates ranged from 0.92 to 6.83, the deep SSI rates ranged from 0.34 to 1.86, and the adjusted deep hospital-specific SSI rates ranged from 0.37 to 1.85. For KPRO, the overall SSI rates ranged from 0.71 to 5.03, the deep SSI rates ranged from 0.42 to 1.60, and the adjusted deep hospital-specific SSI rates ranged from 0.56 to 1.55. For both procedures, the 95% CIs of the rates between hospitals largely overlapped; only single outliers were detected. Hospital rank did not correlate between overall and deep SSI rates (HPRO, ρ=0.03; KPRO, ρ=0.40), but a correlation was observed in hospital rank for deep and adjusted deep SSI rates (HPRO, ρ=0.85; KPRO, ρ=0.94). CONCLUSION Deep SSI rates may be a better tool for interhospital comparisons than overall SSI rates. Although the adjustment could lead to fairer hospital ranking, it is not always necessary for feedback. Infect Control Hosp Epidemiol 2017;38:423-429.

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Section: Discussionmentioning

confidence: 99%

Interhospital Comparison of Surgical Site Infection Rates in Orthopedic Surgery

Skufca

Ollgren

Virtanen

et al. 2017

Infect. Control Hosp. Epidemiol.

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“…Despite the use of this risk index in many countries, the NNIS risk index has several drawbacks, including the following: using only 4 factors (including the American Society of Anesthesiologists score, wound class, procedure duration, and endoscope use) to calculate the risk index and its inadequacy, considering all variables as binary variables and not quantitative variables, and considering the same weights for different procedures and their inaccuracy (different weights are needed). For this reason, in 2009, the United States National Healthcare Safety Network (NHSN) introduced a new statistical model to replace the NNIS risk index to estimate the expected infection incidence for all infections, which was then used as a denominator in the new measurement: the standardized infection ratio (SIR) based on a statistical model [ 9 , 10 ]. The use of a statistical model–based SIR solved the problems of utilizing an SIR based on the conventional risk index in the United States and significantly improved international comparisons in this field [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

The Standardization of Hospital-Acquired Infection Rates Using Prediction Models in Iran: Observational Study of National Nosocomial Infection Registry Data

Izadi¹,

Etemad²,

Mehrabi³

et al. 2021

JMIR Public Health Surveill

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Background Many factors contribute to the spreading of hospital-acquired infections (HAIs). Objective This study aimed to standardize the HAI rate using prediction models in Iran based on the National Healthcare Safety Network (NHSN) method. Methods In this study, the Iranian nosocomial infections surveillance system (INIS) was used to gather data on patients with HAIs (126,314 infections). In addition, the hospital statistics and information system (AVAB) was used to collect data on hospital characteristics. First, well-performing hospitals, including 357 hospitals from all over the country, were selected. Data were randomly split into training (70%) and testing (30%) sets. Finally, the standardized infection ratio (SIR) and the corrected SIR were calculated for the HAIs. Results The mean age of the 100,110 patients with an HAI was 40.02 (SD 23.56) years. The corrected SIRs based on the observed and predicted infections for respiratory tract infections (RTIs), urinary tract infections (UTIs), surgical site infections (SSIs), and bloodstream infections (BSIs) were 0.03 (95% CI 0-0.09), 1.02 (95% CI 0.95-1.09), 0.93 (95% CI 0.85-1.007), and 0.91 (95% CI 0.54-1.28), respectively. Moreover, the corrected SIRs for RTIs in the infectious disease, burn, obstetrics and gynecology, and internal medicine wards; UTIs in the burn, infectious disease, internal medicine, and intensive care unit wards; SSIs in the burn and infectious disease wards; and BSIs in most wards were >1, indicating that more HAIs were observed than expected. Conclusions The results of this study can help to promote preventive measures based on scientific evidence. They can also lead to the continuous improvement of the monitoring system by collecting and systematically analyzing data on HAIs and encourage the hospitals to better control their infection rates by establishing a benchmarking system.

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“…Much work done to address the challenge of effective prediction of nosocomial infections. Several techniques using rule-based approaches [18–20], Bayesian Networks [10, 21], Ontologies [22], landmark competing risk prediction models [23], statistical models [24–27], case based reasoning [28, 29], and others [30–33] have been suggested and implemented in the past. The problem of identifying new, unanticipated, and useful patterns in public health surveillance and hospital infection control data is discussed in [19].…”

Section: Introductionmentioning

confidence: 99%

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

Noaman

Nadeem

Ragab

et al. 2017

BioMed Research International

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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.

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The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model–Based Standardized Infection Ratio

Abstract: Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.

Cited by 11 publications

References 25 publications

Interhospital Comparison of Surgical Site Infection Rates in Orthopedic Surgery

Interhospital Comparison of Surgical Site Infection Rates in Orthopedic Surgery

The Standardization of Hospital-Acquired Infection Rates Using Prediction Models in Iran: Observational Study of National Nosocomial Infection Registry Data

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

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