Derivation and validation of a risk-stratification model for patients with probable or proven COVID-19 in EDs: the revised HOME-CoV score

25 Derivation and Validation of a Clinical Decision Rule to Risk Stratify Emergency Department Patients Diagnosed With Seasonal Influenza

This study aimed to develop and validate a predictive model for differentiating central from peripheral etiologies in patients with isolated vestibular syndrome (VS). In this multicenter retrospective cohort study, 506 patients with isolated VS from five hospitals were divided into derivation (n = 301) and validation (n = 205) cohorts. Multivariable logistic regression was performed to determine independent predictors of central VS. These predictors were assigned weights to construct the SAV3E score. The performance of the SAV3E was assessed using the area under the curve (AUC), calibration, and decision curve analysis (DCA) and compared with that of the TriAGe+ and STANDING models. The SAV3E score incorporated five predictors: absence of vestibular vagal symptoms (OR, 0.233; 95% CI, 0.084-0.648; p = 0.005), prior stroke (OR, 15.204; 95% CI, 4.455-51.884; p < 0.001), ABCD2 score of 4-7 (OR, 1.903; 95% CI, 1.206-3.004; p = 0.006), central video oculography nystagmus (OR, 38.377; 95% CI, 8.631-170.644; p < 0.001), and positive video head impulse test (OR, 0.078; 95% CI, 0.033-0.188; p < 0.001). It displayed good discriminative performance with AUCs 0.910 and 0.886 in the derivation and validation cohorts, respectively. It outperformed TriAGe+ (AUC: 0.706) and STANDING (AUC: 0.779) models. Furthermore, calibration analysis revealed good model fit across cohorts (Hosmer-Lemeshow test results: derivation cohort, p = 0.899; validation cohort, p = 0.789). DCA confirmed good clinical utility across a wide range of probability thresholds (derivation cohort: 0.01-0.86, validation cohort: 0.01-1.00). The SAV3E score is a validated tool aimed at differentiating central versus peripheral VS, with the potential to improve diagnostic accuracy for urgent etiologies such as stroke.

Background The Universal Definition of Myocardial Infarction (UDMI) mandates a rise and/or fall in high-sensitivity cardiac troponin (hs-cTn) concentration with at least one measure above the 99th centile of a healthy reference population. However, the 99th centile varies by age, sex, and prevalence of comorbid disease within reference populations, and the application of a single threshold may create diagnostic uncertainty in unselected patients attending the Emergency Department. Purpose To compare performance of hs-cTnI at the 99th centile with a model that includes additional clinical variables, for the diagnosis of type 1 myocardial infarction. Methods The High-Sensitivity Troponin in the Evaluation of patients with Acute Coronary Syndrome (High-STEACS trial) was a stepped wedge cluster randomised controlled trial of 48,282 consecutive patients across 10 hospitals in Scotland. We evaluated the positive predictive value (PPV) of a hs-cTnI &gt;99th centile for a diagnosis of type 1 myocardial infarction. Patients with ST-segment elevation myocardial infarction (STEMI) were excluded, and all were adjudicated according to the 4th UDMI. The study population was randomly divided into derivation (80%) and internal validation (20%) cohorts. Using generalised additive modelling, we tested the effect of adding clinically relevant variables to hs-cTnI for the prediction of type 1 myocardial infarction in the derivation cohort, and assessed performance of the final model in the validation cohort. Results We included 47,101 consecutive patients (61±17 years, 47% female), of whom 9,057 (19%) had at least one hs-cTnI &gt;99th centile (7,207 in derivation and 1,850 in validation cohorts). There were 4,087 (45%) patients with type 1 myocardial infarction, with 3239 (45%) and 848 (46%) in the derivation and validation cohorts, respectively. Across the study population, PPV for type 1 myocardial infarction reduced markedly with increasing age (Figure). Age, sex, chest pain, ischaemia on the electrocardiogram, creatinine and rate of change of hs-cTnI were included in the model. Comorbidities (ischaemic heart disease, diabetes, stroke and hyperlipidaemia) did not improve model performance. In the validation cohort, the area under the curve (AUC) for type 1 myocardial infarction using the 99th centile alone was 0.72 (95% CI 0.70–0.74), whereas the AUC for the optimised model was 0.84 (95% CI 0.82–0.85) (p&lt;0.001 by DeLong's test for difference, see Figure). Figure 1 Conclusion The diagnostic performance of the 99th centile for type 1 myocardial infarction is poor, particularly in older populations. A simple model including readily available clinical features improves diagnostic performance and with further external validation could support more individualised treatment decisions. Acknowledgement/Funding British Heart Foundation

AimsBecause evidence regarding risk stratification predicting prognosis of patients with heart failure (HF) decompensation attended in primary care is lacking, we developed and externally validated a model to forecast death/hospitalization during the first 30 days after an episode of decompensation. The predictive model is based on variables easily obtained in primary care settings.Methods and resultsHEFESTOS is a multinational study consisting of a derivation cohort of HF patients recruited in 14 primary healthcare centres in Barcelona and a validation cohort from primary healthcare in 9 other European countries. The derivation and validation cohorts included 561 and 250 patients, respectively. Percentages of women in the derivation and validation cohorts were 56.3% and 47.6% (P = 0.026), respectively. Mean age was 82.2 years (SD 8.03) in the derivation cohort, and 79.3 years (SD 10.3) in the validation one (P = 0.001). HF with preserved ejection fraction represented 72.1% in the derivation cohort and 58.8% in the validation one (P = 0.004). Mortality/hospitalization during the first 30 days after a decompensation episode was 30.5% and 26% (P = 0.225) for the derivation and validation cohorts, respectively. Multivariable logistic regression models were performed to develop a score of risk. The identified predictors were worsening of dyspnoea [odds ratio (OR): 2.5; P = 0.001], orthopnoea (OR: 2.16; P = 0.01), paroxysmal nocturnal dyspnoea (OR: 2.25; P = 0.01), crackles (OR: 2.35; P = 0.01), New York Heart Association functional class III/IV (OR: 2.11; P = 0.001), oxygen saturation ≤ 90% (OR: 4.98; P < 0.001), heart rate > 100 b.p.m. (OR: 2.72; P = 0.002), and previous hospitalization due to HF (OR: 2.45; P < 0.001). The model showed an area under the curve (AUC) of 0.807, 95% confidence interval (CI): [0.770; 0.845] in the derivation cohort and AUC 0.73, 95% CI: [0.660; 0.808] in the validation one. No significant differences between both cohorts were observed (P = 0.08). Regarding probability of hospitalization/death, three risk groups were defined: low <5%, medium 5–20%, and high >20%. Outcome incidence was 2.7% for the low‐risk group, 12.8% for medium risk, and 46.2% for high risk in the derivation cohort, and 9.1%, 12.9%, and 39.6% in the validation one.ConclusionsThe HEFESTOS score, based on variables easily accessible in a community setting and validated in an external European cohort, properly predicted the risk of death/hospitalization during the first 30 days after an HF decompensation episode.

Background: Venous catheter placement is the most common invasive procedure performed in hospitals. Despite their widespread use and importance in healthcare, these devices can cause complications such as catheter-associated bloodstream infections (CABSIs). Although several studies have investigated potential risk factors, including sociodemographic, medical history, and clinical variables, the results remain inconsistent and inconclusive. Objectives: The aim of this study was to identify independent risk factors for CABSIs and to develop and validate a predictive model for CABSIs in patients with midline catheters, centrally inserted central catheters (CICCs), and peripherally inserted central catheters (PICCs). Methods: We conducted an observational cohort follow-up study including hospitalized patients with a CICC, PICC, or midline catheter between January 2016 and March 2022. Devices were randomly assigned to derivation (n = 6036) and validation (n = 1549) cohorts. Candidate predictors with p < 0.25 in univariate analysis entered a multivariable logistic regression model, and final variables were selected by backward stepwise regression. Performance in the validation cohort was assessed by calibration and discrimination using the Hosmer–Lemeshow test and AUC. Results: The prevalence of CABSIs in the derivation cohort was 1.8%. Independent risk factors for CABSIs included tracheostomy, a history of bacteremia within 3 months before catheter placement, the presence of a synchronous central catheter, active oncohematological disease, and having received total parenteral nutrition (TPN). The presence of these five variables increased the probability of CABSIs to 42.1%. The final model demonstrated good predictive performance with an area under the curve (AUC) of 0.73 in the derivation cohort and 0.77 in the validation cohort. Decision curve analysis showed that the predictive model offered a greater net clinical benefit than the “treat-all” or “treat-none” strategies among threshold probabilities between 0.5% and 5%. Conclusions: The model can help identify high-risk patients, guide risk-based clinical decisions, reduce unnecessary catheter use, and support infection prevention and antimicrobial stewardship.

ObjectiveDevelopment and validation of clinical prediction model (CPM) for serious bacterial infections (SBIs) in children presenting to the emergency department (ED) with febrile illness, based on clinical variables, clinician's “gut feeling,” and “sense of reassurance.Materials and MethodsFebrile children presenting to the ED of Children's Clinical University Hospital (CCUH) between April 1, 2017 and December 31, 2018 were enrolled in a prospective observational study. Data on clinical signs and symptoms at presentation, together with clinician's “gut feeling” of something wrong and “sense of reassurance” were collected as candidate variables for CPM. Variable selection for the CPM was performed using stepwise logistic regression (forward, backward, and bidirectional); Akaike information criterion was used to limit the number of parameters and simplify the model. Bootstrapping was applied for internal validation. For external validation, the model was tested in a separate dataset of patients presenting to six regional hospitals between January 1 and March 31, 2019.ResultsThe derivation cohort consisted of 517; 54% (n = 279) were boys, and the median age was 58 months. SBI was diagnosed in 26.7% (n = 138). Validation cohort included 188 patients; the median age was 28 months, and 26.6% (n = 50) developed SBI. Two CPMs were created, namely, CPM1 consisting of six clinical variables and CPM2 with four clinical variables plus “gut feeling” and “sense of reassurance.” The area under the curve (AUC) for receiver operating characteristics (ROC) curve of CPM1 was 0.744 (95% CI, 0.683–0.805) in the derivation cohort and 0.692 (95% CI, 0.604–0.780) in the validation cohort. AUC for CPM2 was 0.783 (0.727–0.839) and 0.752 (0.674–0.830) in derivation and validation cohorts, respectively. AUC of CPM2 in validation population was significantly higher than that of CPM1 [p = 0.037, 95% CI (−0.129; −0.004)]. A clinical evaluation score was derived from CPM2 to stratify patients in “low risk,” “gray area,” and “high risk” for SBI.ConclusionBoth CPMs had moderate ability to predict SBI and acceptable performance in the validation cohort. Adding variables “gut feeling” and “sense of reassurance” in CPM2 improved its ability to predict SBI. More validation studies are needed for the assessment of applicability to all febrile patients presenting to ED.

A novel cardiac arrest severity score for the early prediction of hypoxic-ischemic brain injury and in-hospital death

Funding Acknowledgements Type of funding sources: None. Background Out-of-hospital cardiac arrest (OHCA) outcomes are unsatisfactory despite postcardiac arrest care. Early prediction of prognoses might help stratify patients and provide tailored therapy. Purpose In this study, we derived and validated a novel scoring system to predict hypoxic-ischaemic brain injury (HIBI) and in-hospital death (IHD). Methods We retrospectively analysed Korean Hypothermia Network prospective registry data collected from in Korea between 2015 and 2018. Patients without neuroprognostication data were excluded, and the remaining patients were randomly divided into derivation and validation cohorts. HIBI was defined when at least one prognostication predicted a poor outcome. IHD meant all deaths regardless of cause. In the derivation cohort, stepwise multivariate logistic regression was conducted for HIBI and IHD scores, and model performance was assessed. We then classified patients into four categories and analysed associations between the categories and cerebral performance categories (CPCs) at hospital discharge. Finally, we validated our models in the internal validation cohort. Results Among 1373 patients, 240 were excluded, and 1133 were randomised into derivation (n=754) and validation cohorts (n=379). In the derivation cohort, 7 and 8 predictors were selected for HIBI (0–8) and IHD scores (0–11), respectively, and the area under the curve (AUC) was 0.85 (95% CI 0.82–0.87) and 0.80 (95% CI 0.77–0.82), respectively. Applying optimum cutoff values of ≥6 points for HIBI and ≥7 points for IHD, patients were classified as follows: HIBI (-)/IHD (-), Category 1 (n=424); HIBI (-)/IHD (+), Category 2 (n=100); HIBI (+)/IHD (-), Category 3 (n=21); and HIBI (+)/IHD (+), Category 4 (n=209). CPCs at discharge were significantly different in each category (p&amp;lt;0.001). In the validation cohort, the model showed moderate discrimination (AUC 0.83, 95% CI 0.79–0.87 for HIBI and AUC 0.77, 95% CI 0.72–0.81 for IHD) with good calibration. Each category of the validation cohort showed a significant difference in discharge outcomes (p&amp;lt;0.001) and a similar trend to the derivation cohort. Conclusions We presented a novel approach for assessing illness severity after OHCA. Although external prospective studies are warranted, risk stratification for HIBI and IHD could help provide OHCA patients with appropriate treatment.

Postoperative atrial fibrillation (POAFib) occurs in 20 to 40% of patients following cardiac surgery, and is associated with an increased perioperative morbidity and mortality. We aimed to develop and validate a simple clinical risk model for the prediction of POAFib after cardiac surgery. An analytical single center retrospective cohort study was conducted, including consecutive patients undergoing cardiac surgery between 2004 and 2017 with POAFib. To create the predictive risk score, a logistic regression model was performed using a random sample of 75% of the population. Coefficients of the model were then converted to a numerical risk score, and three groups were defined: low risk (≤1 point), intermediate risk (2-5 points) and high risk (≥6 points). The score was validated using the remaining 25% of the patients. Discrimination was evaluated through the area under the curve (AUC) ROC, and calibration using the Hosmer-Lemeshow (HL) test, calibration plots, and ratio of expected and observed events (E/O). Six thousand five hundred nine patients underwent cardiac surgery: 52% coronary artery bypass grafting (CABG), 20% valve surgery, 14% combined (CABG and valve surgery) and 12% other. New-onset AF occurred in 1222 patients (18.77%). In the multivariate analysis, age, use of cardiopulmonary bypass pump, severe reduction in left ventricular ejection fraction (LVEF), chronic renal disease and heart failure were independent risk factors for POAFib, while the use of statins was a protective factor. The NOPAF score was calculated by adding points for each independent risk predictor. In the derivation cohort, the AUC was 0.71 (CI95% 0.69-0.72), and in the validation cohort the model also showed good discrimination (AUC 0.67 IC 0.64-0.70) and excellent calibration (HL P = 0.24). The E/O ratio was 1 (CI 95%: 0.89-1.12). According to the risk category, POAFib occurred in 5% of low; 11% of intermediate and 27.7% of high risk patients in the derivation cohort (P <0.001), and 5.7%; 12.6%; and 23.6% in the validation cohort respectively (P <0.001). From a large hospitalized population, we developed and validated a simple risk score named NOPAF, based on clinical variables that accurately stratifies the risk of POAFib. This score may help to identify high-risk patients prior to cardiac surgery, in order to strengthen postoperative atrial fibrillation prophylaxis.

To develop a simplified MRI-based model to predict the risk for positive surgical margins (PSMs) after radical prostatectomy (RP) in patients with prostate cancer (PCa). Consecutive patients who underwent RP for PCa were retrospectively identified from a tertiary referral hospital. Patients who underwent RP between January 2014 and June 2014 were assigned as derivation cohort (n = 330) and those between January 2018 and February 2018 were assigned as validation cohort (n = 100). MRI-based predictors associated with PSM were assessed: tumor size, tumor-capsule contact length, the Prostate Imaging Reporting and Data System (PI-RADS) category, tumor location (tumor contact to the apex or posterolateral side near the neurovascular bundle), apical depth, and prostate volume. A prediction model was developed by using multivariable logistic regression, and then it was transformed into a scoring system. The prediction and calibration performance of this scoring system was evaluated using the C statistics and Hosmer-Lemeshow goodness-of-fit test. A total of 121 (36.7%) and 32 (32.0%) of patients in the derivation and validation cohorts had PSMs after RP. The scoring system consisted of the following variables: tumor-capsule contact length, PI-RADS category, tumor located at the apex and/or posterolateral side. This scoring system provided good prediction performance for PSM in the derivation (C statistics, 0.80 [95% CI: 0.76, 0.85]) and validation (C statistics, 0.77 [95% CI: 0.68, 0.87]) cohorts, and also showed good calibration in both cohorts (p = 0.83 and 0.86, respectively). An MRI-based scoring system can help estimate the risk of PSM after RP. • An MRI-based scoring system served as a tool to estimate the risk of positive surgical margin (C statistics, 0.80 and 0.77 in the derivation and validation cohorts, respectively) after radical prostatectomy. • Tumor with contact to the apex or posterolateral aspect, the tumor contact length to capsule, and higher PI-RADS category were independent predictors for the presence of positive resection margins after radical prostatectomy in men with prostate cancer. • High-risk patients as determined by the scoring system demonstrated adverse post-surgical outcomes compared with low- or intermediate-risk patients, in regard to longer length (mean length, 13.0 mm versus 3.9 mm in low risk or 6.2 mm in intermediate risk; p ≤ 0.001) and higher Gleason grade at the margin (grades 4 and 5 in 69.4% and 20.4% versus 16.7% and 16.7% in low risk or 46.7% and 5.4% in intermediate risk; p < 0.001).

ObjectiveWe developed and validated a pragmatic risk assessment tool for identifying contraceptive discontinuation among Kenyan women who do not desire pregnancy. Study designWithin a prospective cohort of contraceptive users, participants were randomly allocated to derivation (n = 558) and validation (n = 186) cohorts. Risk scores were developed by selecting the Cox proportional hazards model with the minimum Akaike information criterion. Predictive performance was evaluated using time-dependent receiver operating characteristic curves and area under the curve (AUC). ResultsThe overall contraceptive discontinuation rate was 36.9 per 100 woman-years (95% confidence interval [CI] 30.3–44.9). The predictors of discontinuation selected for the risk score included use of a short-term method or copper intrauterine device (vs. injectable or implant), method continuation or switch (vs. initiation), <9 years of completed education, not having a child aged <6 months, and having no spouse or a spouse supportive of family planning (vs. having a spouse who has unsupportive or uncertain attitudes towards family planning). AUC at 24 weeks was 0.76 (95% CI 0.64–0.87) with 70.0% sensitivity and 78.6% specificity at the optimal cut point in the derivation cohort. Discontinuation was 3.8-fold higher among high- vs. low-risk women (95% CI 2.33–6.30). AUC was 0.68 (95% CI 0.47–0.90) in the validation cohort. A simplified score comprising routinely collected variables demonstrated similar performance (derivation-AUC: 0.73 [95% CI 0.60–0.85]; validation-AUC: 0.73 [95% CI 0.51–0.94]). Positive predictive value in the derivation cohort was 31.4% for the full and 28.1% for the simplified score. ConclusionsThe risk scores demonstrated moderate predictive ability but identified large proportions of women as high risk. Future research is needed to improve sensitivity and specificity of a clinical tool to identify women at high risk for experiencing method-related challenges. ImplicationsContraceptive discontinuation is a major driver of unmet contraceptive need globally. Few tools exist for identifying women who may benefit most from additional support in order to meet their contraceptive needs and preferences. This study developed and assessed the validity of a provider-focused risk prediction tool for contraceptive discontinuation among Kenyan women using modern contraception. High rates of early discontinuation observed in this study emphasize the necessity of investing in efforts to develop new contraceptive technologies and stronger delivery systems to better align with women's needs and preferences for voluntary family planning.

To develop and validate a lesion-based grading system using clinicopathological and MRI features for predicting positive surgical margin (PSM) following robotic-assisted laparoscopic prostatectomy (RALP) among prostate cancer (PCa) patients. Consecutive MRI examinations of patients undergoing RALP for PCa were retrospectively collected from two medical institutions. Patients from center 1 undergoing RALP between January 2020 and December 2021 were included in the derivation cohort and those between January 2022 and December 2022 were allocated to the validation cohort. Patients from center 2 were assigned to the test cohort. PSM associated imaging and clinicopathological predictors were assessed. A grading system was developed through fixed effect logistic regression and classification and regression tree analysis. The area under the curve (AUC), sensitivity and specificity were calculated and compared by Delong test and McNemar test. A total 489 lesions from 396 patientswere included and 82 (29.1%), 32 (35.6%) and 42 (35.9%) of lesions were observed PSM after RALP in the derivation, validation and test cohorts, respectively. The grading system comprised tumor morphology, tumor location, anatomical feature and clinical risk stratification. The grading system demonstrated good prediction performance for PSM in the derivation (AUC 0.82 [95% CI: 0.77, 0.86]), validation (AUC 0.76 [95% CI: 0.66, 0.85]) and test (AUC 0.81 [95% CI: 0.72, 0.88]) cohorts. When compared with Park's model (AUC: 0.73 [95% CI: 0.64, 0.81]) in the test cohort, our grading system demonstrated significantly higher AUC and specificity (P < 0.05). The lesion-based grading system can assess the likelihood of PSM after RALP, assisting surgeons in minimizing the occurrence rate of PSM while optimizing functional preservation.

Background: Patients presenting with transient or minor ischemic symptoms (TIA/MIS) are at risk for early deterioration. Identification of those at highest risk for progression may justify more aggressive acute reperfusion treatments. We tested the hypothesis that baseline perfusion (PWI)- diffusion (DWI) mismatch predicts clinical deterioration and infarct growth on follow-up imaging in this population. Methods: Patients with TIA/MIS (NIH Stroke Scale ≤ 3) were prospectively enrolled and imaged within 24 hours of symptom onset as part of two sequential prospective imaging studies. All patients had clinical follow-up. Baseline DWI and PWI (tmax+4s delay) and follow-up FLAIR infarct volumes (day 30 (derivation), day 90 (validation) cohort) were measured. Mismatch volumes were calculated as (Tmax+4s delay) - DWI lesion volume. Primary outcome was infarct growth on FLAIR imaging which was defined a priori as growth of at least 2.5 ml. Secondary outcome was clinical progression. Results: 137 patients were included in the derivation and 281 patients in the validation cohorts. The rates of DWI (54% vs 56%, p= 0.67) and PWI lesions (42% vs 34.5%, p=0.16) at baseline were similar between the cohorts. The median time between symptom onset and baseline imaging was significantly shorter in the derivation (9.2 h, IQR=9.4) relative to the validation sets (15.1h, IQR=12.5 p&lt;0.001). More patients had follow-up imaging in the derivation (87%) compared to the validation (76%) cohort (p=0.021). Primary and secondary outcome occurred in 18.5% and 9.5% in the derivation and 5.5% and 4.6% in the validation cohort. In the derivation cohort, baseline mismatch volumes adjusting for age, sex and time from symptom onset to MRI significantly predicted radiographic progression (OR=1.06 [1.03-1.09], p&lt;0.001). The optimal threshold for maximizing sensitivity (Sen) and specificity (Spec) in predicting infarct growth occurred at a mismatch volume of 10ml; which correctly predicted infarct expansion with 82% (Sen) and 91%(Spec) (Area under the curve (AUC)= 0.89 [0.80-0.98]). In the validation cohort, this threshold was highly predictive of radiological progression (p=0.011, McNemars test). Linear regression showed that for every 10ml of mismatch, there would be 2.5ml infarct growth on day 30 FLAIR [R=0.80, p&lt;0.001] (derivation set) and 1.1 ml of growth on day 90 FLAIR (R=0.22, p&lt;0.001) (validation set). Baseline mismatch showed a high discriminative value in predicting clinical deterioration in the derivation (AUC =0.81 [0.67-0.96]) and moderate value in the validation cohort (AUC=0.66 [0.46, 0.85]). Conclusion: In a population of patients with minor stroke and TIA, early MR perfusion-diffusion mismatch predicts infarct growth and clinical progression. These findings suggest that there may be a group of patients with minor symptoms in whom reperfusion strategies may be beneficial.

FRI0595 CAN A SINGLE QUESTION ON FUNCTIONAL IMPAIRMENTS FACILITATE THE IDENTIFICATION OF EARLY INFLAMMATORY ARTHRITIS? A LARGE CROSS-SECTIONAL DERIVATION AND VALIDATION STUDY

Limitations to Study on Noninvasive Ventilation