Clinical prediction rules for childhood UTIs: a cross-sectional study in ambulatory care

BackgroundDiagnosing childhood urinary tract infections (UTI) is challenging.AimValidate clinical prediction rules (UTIcalc, DUTY, Gorelick) for paediatric UTIs in primary care.Design & settingPost-hoc analysis of a cross-sectional study in 39 general practices and 2 emergency departments (Belgium, March 2019 to March 2020).MethodPhysicians recruited acutely ill children ≤18 years and sampled urine systematically for culture. Per rule, we performed an apparent validation; calculated sensitivities and specificities with 95%CI per threshold in the target group. For the DUTY coefficient-based algorithm, we performed a logistic calibration and calculated the Area Under the Curve with 95%CI.ResultsOf 834 children ≤18 years recruited, there were 297 children <5 years. The UTIcalc and Gorelick score had high to moderate sensitivity and low specificity (UTIcalc ≥2%) 75%; and 16% respectively; Gorelick (≥2 variables) 91%; and 8%. In contrast, the DUTY score ≥5 points had low sensitivity (8%), but high specificity (99%). Urine samples would be obtained in 72% vs 38% (UTIcalc), 92% vs 38% (Gorelick) or 1% vs 32% (DUTY) of children, compared to routine care. The number of missed infections per score was 1/4 (UTIcalc), 2/23 (Gorelick) and 24/26 (DUTY). The UTIcalc+ dipstick model had high sensitivity and specificity (100%; and 91%); resulting in no missed cases and 59% (95%CI 49%–68%) of antibiotics prescribed inappropriately.ConclusionIn this study, the UTIcalc and Gorelick score were useful for ruling out UTI but resulted in high urine sampling rates. The DUTY score had low sensitivity, meaning that 92% of UTIs would be missed.

Significance of the Modified NUTRIC Score for Predicting Clinical Outcomes in Patients with Severe Community-Acquired Pneumonia

Nutritional status could affect clinical outcomes in critical patients. We aimed to determine the prognostic accuracy of the modified Nutrition Risk in Critically Ill (mNUTRIC) score for hospital mortality and treatment outcomes in patients with severe community-acquired pneumonia (SCAP) compared to other clinical prediction rules. We enrolled SCAP patients in a multi-center setting retrospectively. The mNUTRIC score and clinical prediction rules for pneumonia, as well as clinical factors, were calculated and recorded. Clinical outcomes, including mortality status and treatment outcome, were assessed after the patient was discharged. We used the receiver operating characteristic (ROC) curve method and multivariate logistic regression analysis to determine the prognostic accuracy of the mNUTRIC score for predicting clinical outcomes compared to clinical prediction rules, while 815 SCAP patients were enrolled. ROC curve analysis showed that the mNUTRIC score was the most effective at predicting each clinical outcome and had the highest area under the ROC curve value. The cut-off value for predicting clinical outcomes was 5.5. By multivariate logistic regression analysis, the mNUTRIC score was also an independent predictor of both clinical outcomes in SCAP patients. We concluded that the mNUTRIC score is a better prognostic factor for predicting clinical outcomes in SCAP patients compared to other clinical prediction rules.

Development of Clinical Prediction Rules for One-Year Postoperative Functional Outcome in Patients with Intertrochanteric Fractures: The Intertrochanteric Fracture Ambulatory Prediction (IT-AP) Tool

Individualized prediction of postoperative ambulatory status for patients with intertrochanteric fractures is clinically relevant, during both preoperative and intraoperative periods. This study intended to develop clinical prediction rules (CPR) to predict one-year postoperative functional outcomes in patients with intertrochanteric fractures. CPR development was based on a secondary analysis of a retrospective cohort of patients with intertrochanteric fractures aged ≥50 years who underwent a surgical fixation. Good ambulatory status was defined as a New Mobility Score ≥5. Two CPR for preoperative and intraoperative predictions were derived using clinical profiles and surgical-related parameters using logistic regression with the multivariable fractional polynomial procedure. In this study, 221 patients with intertrochanteric fractures were included. Of these, 160 (72.4%) had good functional status at one year. The preoperative model showed an acceptable AuROC of 0.77 (95%CI 0.70 to 0.85). After surgical-related parameters were incorporated into the preoperative model, the model discriminative ability was significantly improved to an AuROC of 0.83 (95%CI 0.77 to 0.88) (p = 0.021). The newly-derived CPR enable physicians to provide patients with intertrochanteric fractures with their individualized predictions of functional outcome one year after surgery, which could be used for risk communication, surgical optimization and tailoring postoperative care that fits patients’ expectations.

A Cost-Effective and Non-Invasive pfeRNA-Based Test Differentiates Benign and Suspicious Pulmonary Nodules from Malignant Ones

The ability to differentiate between benign, suspicious, and malignant pulmonary nodules is imperative for definitive intervention in patients with early stage lung cancers. Here, we report that plasma protein functional effector sncRNAs (pfeRNAs) serve as non-invasive biomarkers for determining both the existence and the nature of pulmonary nodules in a three-stage study that included the healthy group, patients with benign pulmonary nodules, patients with suspicious nodules, and patients with malignant nodules. Following the standards required for a clinical laboratory improvement amendments (CLIA)-compliant laboratory-developed test (LDT), we identified a pfeRNA classifier containing 8 pfeRNAs in 108 biospecimens from 60 patients by sncRNA deep sequencing, deduced prediction rules using a separate training cohort of 198 plasma specimens, and then applied the prediction rules to another 230 plasma specimens in an independent validation cohort. The pfeRNA classifier could (1) differentiate patients with or without pulmonary nodules with an average sensitivity and specificity of 96.2% and 97.35% and (2) differentiate malignant versus benign pulmonary nodules with an average sensitivity and specificity of 77.1% and 74.25%. Our biomarkers are cost-effective, non-invasive, sensitive, and specific, and the qPCR-based method provides the possibility for automatic testing of robotic applications.

Knowledge translation of prediction rules: methods to help health professionals understand their trade-offs

Clinical prediction models are developed with the ultimate aim of improving patient outcomes, and are often turned into prediction rules (e.g. classifying people as low/high risk using cut-points of predicted risk) at some point during the development stage. Prediction rules often have reasonable ability to either rule-in or rule-out disease (or another event), but rarely both. When a prediction model is intended to be used as a prediction rule, conveying its performance using the C-statistic, the most commonly reported model performance measure, does not provide information on the magnitude of the trade-offs. Yet, it is important that these trade-offs are clear, for example, to health professionals who might implement the prediction rule. This can be viewed as a form of knowledge translation. When communicating information on trade-offs to patients and the public there is a large body of evidence that indicates natural frequencies are most easily understood, and one particularly well-received way of depicting the natural frequency information is to use population diagrams. There is also evidence that health professionals benefit from information presented in this way.Here we illustrate how the implications of the trade-offs associated with prediction rules can be more readily appreciated when using natural frequencies. We recommend that the reporting of the performance of prediction rules should (1) present information using natural frequencies across a range of cut-points to inform the choice of plausible cut-points and (2) when the prediction rule is recommended for clinical use at a particular cut-point the implications of the trade-offs are communicated using population diagrams. Using two existing prediction rules, we illustrate how these methods offer a means of effectively and transparently communicating essential information about trade-offs associated with prediction rules.

Prediction of antimicrobial resistance in clinical Enterococcus faecium isolates using a rules-based analysis of whole genome sequences

Background: Enterococcus faecium is a major cause of clinical infections, often due to multidrug-resistant (MDR) strains. Whole genome sequencing (WGS) is a powerful tool to study MDR bacteria and their antimicrobial resistance (AMR) mechanisms. Here we use WGS to characterize E. faecium clinical isolates and test the feasibility of rules-based genotypic prediction of AMR. Methods: Clinical isolates were divided into derivation and validation sets. Phenotypic susceptibility testing for ampicillin, vancomycin, high-level gentamicin, ciprofloxacin, levofloxacin, doxycycline, tetracycline, and linezolid was performed using the VITEK 2 automated system, with confirmation and discrepancy resolution by broth microdilution, disk diffusion, or gradient diffusion when needed. WGS was performed to identify isolate lineage and AMR genotype. AMR prediction rules were derived by analyzing the genotypic-phenotypic relationship in the derivation set. Results: Phylogenetic analysis demonstrated that 88% of isolates in the collection belonged to hospital-associated clonal complex 17. Additionally, 12% of isolates had novel sequence types. When applied to the validation set, the derived prediction rules demonstrated an overall positive predictive value of 98% and negative predictive value of 99% compared to standard phenotypic methods. Most errors were falsely resistant predictions for tetracycline and doxycycline. Further analysis of genotypic-phenotypic discrepancies revealed potentially novel pbp5 and tet (M) alleles that provide insight into ampicillin and tetracycline class resistance mechanisms. The prediction rules demonstrated generalizability when tested on an external dataset. Conclusions: Known AMR genes and mutations can predict E. faecium phenotypic susceptibility with high accuracy for most routinely tested antibiotics, providing opportunities for advancing molecular diagnostics.