Prevalence dependent calibration of a predictive model for nasal carriage of methicillin-resistant Staphylococcus aureus

Abstract

BackgroundPublished models predicting nasal colonization with Methicillin-resistant Staphylococcus aureus among hospital admissions predominantly focus on separation of carriers from non-carriers and are frequently evaluated using measures of discrimination. In contrast, accurate estimation of carriage probability, which may inform decisions regarding treatment and infection control, is rarely assessed. Furthermore, no published models adjust for MRSA prevalence.MethodsUsing logistic regression, a scoring system (values from 0 to 200) predicting nasal carriage of MRSA was created using a derivation cohort of 3091 individuals admitted to a European tertiary referral center between July 2007 and March 2008. The expected positive predictive value of a rapid diagnostic test (GeneOhm, Becton & Dickinson Co.) was modeled using non-linear regression according to score. Models were validated on a second cohort from the same hospital consisting of 2043 patients admitted between August 2008 and January 2012. Our suggested correction score for prevalence was proportional to the log-transformed odds ratio between cohorts. Calibration before and after correction, i.e. accurate classification into arbitrary strata, was assessed with the Hosmer-Lemeshow-Test.ResultsTreating culture as reference, the rapid diagnostic test had positive predictive values of 64.8% and 54.0% in derivation and internal validation corhorts with prevalences of 2.3% and 1.7%, respectively. In addition to low prevalence, low positive predictive values were due to high proportion (> 66%) of mecA-negative Staphylococcus aureus among false positive results. Age, nursing home residence, admission through the medical emergency department, and ICD-10-GM admission diagnoses starting with “A” or “J” were associated with MRSA carriage and were thus included in the scoring system, which showed good calibration in predicting probability of carriage and the rapid diagnostic test’s expected positive predictive value. Calibration for both probability of carriage and expected positive predictive value in the internal validation cohort was improved by applying the correction score.ConclusionsGiven a set of patient parameters, the presented models accurately predict a) probability of nasal carriage of MRSA and b) a rapid diagnostic test’s expected positive predictive value. While the former can inform decisions regarding empiric antibiotic treatment and infection control, the latter can influence choice of screening method.

Development and external validation of a clinical prediction model for MRSA carriage at hospital admission in Southeast Lower Saxony, Germany

“…Age was the next important variable in our stability analysis. Increased age is a well-known predictor for MRSA carriage in many other studies [19][20][21][22] , which was also confirmed in the univariable analysis in our study. Despite the fact that age was included in many of the models in the stability analysis, we decided not to use age for the prediction model, because age and care dependency were selected mutually exclusive in the bootstrap runs and Table 3.…”

“…This is in line with other studies where a history of MRSA carriage was often the strongest predictor 17,18 . In addition, high age [19][20][21] and parameters associated with "contact with healthcare" 22 , such as history of antibiotic therapy or inpatient treatment, presence of chronic diseases, living in a long-term care facility, dialysis or skin disease were regularly described as risk factors for MRSA carriage [19][20][21][22][23] .…”

“…Such a score is very easy to apply in clinical practice as information on all three risk factors usually is readily available at the time of admission. Thus, there is no need for access to additional data sources, as it has been the case for predictive models developed in other studies 19 , 21 , 22 , 25 .…”

Prevalence and Molecular Epidemiology of Staphylococcus aureus among Residents of Seven Nursing Homes in Shanghai

“…It was suggested that modifying cleaning practices may reduce both MRSA environmental contamination and patient burden among NHs[ 37 ], and that the best practices of infection control and MRSA surveillance information should in fact be shared among hospitals and nursing homes[ 38 ]. Another modeling study suggests that models have the ability to accurately predict the probability of nasal carriage of MRSA as well as positive predictive values of various rapid diagnostic tests when given a set of resident parameters[ 39 ]. Last but not least, empiric antibiotic treatment, infection control measures and screening method may be applied to rectify colonization of S .…”

Prospective Validation of an Electronic Health Record–Based, Real-Time Suicide Risk Model

“…34 To enrich signal, the research-grade model 20 was trained with a sample of the larger population, increasing potential miscalibration. We anticipated miscalibration and corrected it with logistic calibration, 35,36 a univariate logistic regression model with uncalibrated predictions trained on outcomes from June to October to recalibrate predictions from November to April.…”