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Background: The Lafontaine criteria are the most commonly cited criteria for predicting unstable distal radius fractures. However, formal validation of the performance of these criteria remains limited. Therefore, we aimed to evaluate the Lafontaine criteria as a diagnostic prediction rule for distal radius fractures, assess the inter-rater reliability and predictive ability of various parameters for distal radius instability, and develop new criteria for fracture instability using reliable and highly predictive factors. Methods: This retrospective study included 274 adult patients with acute distal radius fractures treated with closed reduction and immobilisation between January 2019 and December 2022. Patients who underwent immediate surgery, were lost to follow-up before 4 weeks, or had unacceptable alignment after reduction were excluded. The Lafontaine criteria were validated using the area under the receiver operating characteristic curve (AUROC). Criteria with an AUROC > 0.7 were considered acceptable. The criteria were updated using risk factors with stronger associations in the multivariable logistic regression analysis, and the inter-rater reliability of potential predictors was evaluated. Results: The median age of the patients was 63 years; 78% were female. Redisplacement occurred in 39% of the cases. The AUROC for the Lafontaine criteria was 0.65 (95% confidence interval [CI] 0.57–0.74). Multivariable logistic regression showed that age 56–74 years (odds ratio [OR] 3.92, 95% CI 1.82–9.16, p < 0.001) age > 74 years (OR 6.34, 95% CI 2.66–16.2, p < 0.001), associated ulna fracture (OR 1.61, 95% CI 0.92–2.84, p = 0.10), and initial radial shortening > 3 mm (OR 5.78, 95% CI 3.11–11.2, p < 0.001) were the strongest predictive factors of fracture instability. These predictors demonstrated substantial inter-rater reliability, making them suitable for clinical use. Updating the model with these risk factors resulted in an AUROC of 0.74 (95% CI 0.66–0.82). Conclusions: The performance of the Lafontaine criteria in discriminating unstable distal radius fractures was unacceptable in our study cohort. The updated criteria using age group (< 56 years, 56–74 years, and > 74 years), associated ulnar fractures, and initial radial shortening > 3 mm was found to have moderate discrimination; however, further research is warranted to improve the prediction and measurement reliability of fracture instability. [ABSTRACT FROM AUTHOR]

Background: Definition of chest X-ray (CXR) features associated with laboratory-confirmed pneumocystis pneumonia (PCP) among HIV-positive adults is needed to improve diagnosis in high-burden settings. Objectives: Our primary objective was to identify CXR features associated with confirmed PCP diagnosis and severe PCP (defined by hypoxia, intensive care unit referral/admission, and/or in-hospital death). We also explored the performance of logistic regression models, incorporating selected clinical and CXR predictors, for PCP diagnosis and severe PCP. Method: We conducted a case-control study involving HIV-positive adults with laboratory-confirmed PCP and a matched cohort with non-PCP respiratory presentations at regional hospitals in Cape Town, South Africa (2012–2020). Results: Records from 104 adults (52 PCP cases and 52 non-PCP controls) were included. Diffuse versus patchy ground-glass opacification was associated with increased odds of PCP diagnosis (adjusted odds ratio [aOR]: 6.2, 95% confidence interval [CI]: 1.6–28.9, P = 0.01) and severe PCP (aOR: 4.5, 95% CI: 1.6–14.4, P = 0.008). Consolidation was associated with severe PCP (aOR: 3.3, 95% CI: 1.2–11.0, P = 0.03) as was increasing ground-glass zone involvement (aOR: 2.1 for each one-unit increase in involved zone; 95% CI: 1.4–3.2, P = 0.0004). Models incorporating hypoxia (hypoxia model) or tachypnoea (respiratory rate model) with diffuse ground-glass opacities, absence of pleural effusion or reticular/reticulonodular changes on CXR performed well in predicting PCP (area under the receiver operating characteristic curve 0.828 [hypoxia model] and 0.857 [respiratory rate model]). Conclusion: CXR evaluation alongside bedside clinical information offers good accuracy for discriminating definite PCP from other HIV-associated respiratory diseases.

Current models for predicting Clostridioides difficile infection (CDI) recurrence rates have a limited capacity to account for important risk factors. This study developed a clinical prediction rule for CDI recurrence. This retrospective cohort study evaluated 209 patients with CDI at a university hospital in Japan. Logistic regression and receiver operating characteristic curve analyses were performed to identify potential predictors (age, sex, underlying diseases, antibiotic use, acid suppressants, immunosuppressants, CDI history) of CDI recurrence. Forty-five patients developed recurrent CDI. Univariate analyses identified several significant recurrence predictors (enteral feeding, inflammatory bowel diseases [IBD], community-onset CDI, severe CDI). Enteral feeding (odds ratio: 3.87, 95% confidence interval: 1.75-8.56) and IBD (odds ratio: 7.08, 95% confidence interval: 1.28-39.06) were significant factors in the multivariate analysis. The CHIEF predictive scoring system was developed using 5 relevant variables (carbapenem use, hematologic malignancy, IBD, enteral feeding, fluoroquinolone use); the area under the receiver operating characteristic curve for the CHIEF score was 0.70. The CHIEF score incorporates useful, clinically available factors and could help identify patients at risk of recurrent CDI. These findings contribute to the understanding of risk factors associated with CDI recurrence and provide support for the development of prevention strategies. • A clinical prediction rule for Clostridioides difficile recurrence was established. • Enteral feeding and inflammatory bowel disease were significant factors. • The proposed approach can identify patients at risk of recurrent infection. [ABSTRACT FROM AUTHOR]

Pediatric septic arthritis of the hip (SAH) in children is a severe pathology, requiring prompt diagnosis and treatment to avoid destructive sequelae of the joint. Its diagnosis can be challenging, however, due to its spectrum of manifestations and differential diagnosis. Last century, multiple research teams studied the curves of systemic inflammation markers to aid the differential diagnosis. Kocher showed that a history of fever >38.5 °C, non-weight bearing, an erythrocyte sedimentation rate >40 mm/h, and serum white blood cells >12,000/mm3 were highly suggestive of SAH, with a predicted probability of 99.6% when all these predictors manifested in pediatric patients. Caird validated these criteria, also adding a C-reactive protein >20 mg/L, reaching a 98% probability of SAH when these five criteria were present. The Kocher and the Caird criteria were then applied in multiple settings, but were never clearly validated. Moreover, they were studied and validated in the years when Kingella kingae was just emerging, and this was probably responsible for false-negative cases in multiple centers. For this reason, the Kocher and the Caird criteria are still at the center of a debate on the diagnostic tools for pediatric SAH. We provide a historical overview of the development of clinical and laboratory test algorithms for pediatric SAH. Further, new perspectives for future research on the prediction rules of pediatric SAH are here proposed. [ABSTRACT FROM AUTHOR]

Pediatric septic arthritis of the hip (SAH) in children is a severe pathology, requiring prompt diagnosis and treatment to avoid destructive sequelae of the joint. Its diagnosis can be challenging, however, due to its spectrum of manifestations and differential diagnosis. Last century, multiple research teams studied the curves of systemic inflammation markers to aid the differential diagnosis. Kocher showed that a history of fever >38.5 °C, non-weight bearing, an erythrocyte sedimentation rate >40 mm/h, and serum white blood cells >12,000/mm3 were highly suggestive of SAH, with a predicted probability of 99.6% when all these predictors manifested in pediatric patients. Caird validated these criteria, also adding a C-reactive protein >20 mg/L, reaching a 98% probability of SAH when these five criteria were present. The Kocher and the Caird criteria were then applied in multiple settings, but were never clearly validated. Moreover, they were studied and validated in the years when Kingella kingae was just emerging, and this was probably responsible for false-negative cases in multiple centers. For this reason, the Kocher and the Caird criteria are still at the center of a debate on the diagnostic tools for pediatric SAH. We provide a historical overview of the development of clinical and laboratory test algorithms for pediatric SAH. Further, new perspectives for future research on the prediction rules of pediatric SAH are here proposed.

Background: Gastroenteritis (GE) is a non-specific term for various pathologic states of the gastrointestinal tract. Infectious agents usually cause acute GE. At present, there are no robust decision-making rules that predict bacterial GE and dictate when to start antibiotics for patients suffering from acute GE to the emergency department (ED). We aim to define a clinical prediction rule to aid in the diagnosis of bacterial GE, requiring empirical antibiotics in adult patients presenting to the emergency department with acute GE. Methods: A two-year retrospective case review was performed on all cases from July 2015 to June 2017 that included patients with acute GE symptoms referred to the ED, after which their stool cultures were performed. The clinical parameters analyzed included patient with comorbid conditions, physical examination findings, historical markers, point-of-care and radiographic tests and other laboratory work. We then used multi-variate logistic regression analysis on each group (bacterial culture–positive GE and bacterial culture–negative GE) to elucidate clinical criteria with the highest yield for predicting bacterial gastroenteritis (BGE). Results: A total of 756 patients with a mean age of 52 years, 52% female and 48% male, respectively, were included in the study. On the basis of the data of these patients, we suggested using a scoring system to delineate the need for empirical antibiotics in patients with suspected bacterial GE based on six clinical and laboratory variables. We termed this the BGE score. A score 0 – 2 points suggests low risk (0.9%) of bacterial GE. A score of 3 – 4 points confers an intermediate risk of 12.0% and a score of 5 – 8 points confers a high risk of 85.7%. A cut-off of ≥ 5 points may be used to predict culture-positive BGE with a 75% sensitivity and 75% specificity. The area under the receiver operating characteristic (AUROC) for the scoring system (range 0 – 8) was 0.812 (95% CI: 0.780–0.843) p-value < 0.001. Conclusion: We suggest using the BGE scoring system (cut-off ≥ 5 points) to delineate the need for empirical antibiotics in patients diagnosed with gastroenteritis. While this is a pilot study, which will require further validation with a larger sample size, our proposed decision-making rule will potentially serve to improve the diagnosis of BGE and thus reduce unnecessary prescription of antibiotics, which will in turn reduce antibiotic-associated adverse events and save on costs worldwide. [ABSTRACT FROM AUTHOR]

Predictive modeling is becoming more popular in psychological science. Machine learning techniques have been used to develop prediction rules based on items of psychological tests. However, this approach does not take into account that these items are noisy indicators of the constructs they intend to measure. Structural equation modeling does take this into account. Several authors have concluded that it is impossible to make out-of-sample predictions based on a reflective structural equation model. We show that it is possible to make such predictions and we develop R-code to do so. With two empirical examples, we show that SEM-based prediction can outperform prediction based on linear regression models. With three simulation studies, we further investigate the SEM-based prediction rule and its robustness in comparison with predictions using regularized linear regression. We conclude that the new SEM-based prediction rule is robust against violation of the normality assumption but sensitive to model misspecification. [ABSTRACT FROM AUTHOR]

Objectives: During the COVID-19 pandemic, several studies described an increased chance of developing pulmonary embolism (PE). Several scores have been used to predict the occurrence of PE. This systematic review summarizes the literature on predicting rules for PE in hospitalized COVID-19 patients (HCPs). Methods: PUBMED and EMBASE databases were searched to identify articles (1 January 2020-28 April 2021) presenting data pertaining to the use of a prediction rule to assess the risk for PE in adult HCPs. The investigated outcome was the diagnosis of PE. Studies presenting data using a single laboratory assay for PE prediction were excluded. Included studies were appraised for methodological quality using the Newcastle - Ottawa Quality Assessment Scale for Cohort Studies (NOS). Results: We obtained a refined pool of twelve studies for five scoring systems (Wells score, Geneva score, CHADS2/CHA2DS2VASc/M-CHA2DS2VASc, CHOD score, Padua Prediction Score), and 4,526 patients. Only one score was designed explicitly for HCPs. Three and nine included studies were prospective and retrospective cohort studies, respectively. Among the examined scores, the CHOD score seems promising for predictive ability. Conclusion: New prediction rules, specifically developed and validated for estimating the risk of PE in HCP, differentiating ICU from non-ICU patients, and taking into account anticoagulation prophylaxis, comorbidities, and the time from COVID-19 diagnosis are needed.

Abstract Background The need for invasive mechanical ventilation (IMV) is linked to significant morbidity and mortality in patients with influenza-related pneumonia (Flu-p). We aimed to develop an assessment tool to predict IMV among Flu-p patients within 14 days of admission. Methods In total, 1107 Flu-p patients from five teaching hospitals were retrospectively enrolled from January 2012 to December 2019, including 895 patients in the derivation cohort and 212 patients in the validation cohort. The predictive model was established based on independent risk factors for IMV in the Flu-p patients from the derivation cohort. Results Overall, 10.6% (117/1107) of patients underwent IMV within 14 days of admission. Multivariate regression analyses revealed that the following factors were associated with IMV: early neuraminidase inhibitor use (− 3 points), lymphocytes

[Display omitted] To test the hypothesis that a prediction rule including levels of interleukin-6 in pericardial drainage (pdIL-6) would improve the discrimination in classifying patients undergoing coronary artery bypass grafting (CABG) into different postoperative atrial fibrillation (POAF) risk levels. Prospective cohort study. A university-affiliated tertiary hospital. Patients undergoing CABG. None. We prospectively recruited patients who underwent CABG into derivation and validation cohorts. The independent predictors were identified in the derivation cohort using multiple logistic regression and tested in the validation cohort. The performance of the predictive model was tested using area under the receiver operating characteristic curve (AUC) in both cohorts. A prediction rule was created by assigning points to each predictor. Patients were classified in various risk levels according to their total risk scores. We enrolled 302 and 207 patients in the derivation and validation cohorts, respectively. Multiple logistic regression analysis identified six predictors: age ≥61 y, left atrial diameter ≥49 mm, right atrial diameter ≥45 mm, number of grafts ≥3, and serum uric acid ≥226 µmol/L and pdIL-6 levels ≥166 ng/mL at postoperative 12 h. The AUC of the model was 0.78 and 0.77 for the derivation and validation cohort, respectively, which was greatly increased by adding pdIL-6. Patients were stratified into low-risk, moderate-risk and high-risk groups. A POAF prediction rule including pdIL-6 had good performance for stratifying CABG patients into various risk groups for POAF. The inclusion of pdIL-6 resulted in clinically meaningful improvement in risk prediction. [ABSTRACT FROM AUTHOR]

• Inclusion of post-operative interleukin (IL)-6 improved performance of the postoperative delirium (POD) prediction rule. • The POD prediction rule has a high area under the curve in both derivation and validation cohorts. • Predictors in the rule have a good predictive ability for the development of POD. • The POD prediction rule stratifies patients into different POD risk levels. Patients undergoing coronary artery bypass graft (CABG) are at high risk for developing postoperative delirium (POD). A simple prediction rule may benefit patients from early identification of POD followed by adequate preventive strategies. The purpose of the current study was to develop and validate a POD prediction rule for patients undergoing CABG (POD-CABG), by considering all possible perioperative factors. In this prospective cohort study, patients who underwent first elective isolated CABG were continuously enrolled from May 2014 to November 2015 in a tertiary hospital. Delirium was assessed using the Confusion Assessment Method for Intensive Care Unit. Patients' perioperative risk factors were collected through interviews and review of medical records. The area under receiver-operating characteristic curve (AUC) was used to assess the overall performance of the predictive rule. A total of 242 and 148 patients were enrolled in the derivation and validation cohorts, respectively. Multiple logistic regression analysis identified seven variables that were independently associated with POD: age (≥65 years), gender (female), history of myocardial infarction and diabetes mellitus, postoperative atrial fibrillation, the use of intra-aortic balloon pump, and serum interleukin-6 ≥478 pg/ml at 18 hours after surgery. The AUC of the POD-CABG was 0.84 (95% CI , 0.79-0.90) in the derivation cohort, and was 0.86 (95% CI , 0.80-0.91) after bootstrap resampling. The AUC was 0.81 (95% CI , 0.73-0.88) after the POD-CABG was applied to the validation cohort. The POD-CABG with inclusion of interleukin-6 demonstrated good performance in predicting POD. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract Background Heart failure (HF) is a chronic and common condition with a rising prevalence, especially in the elderly. Morbidity and mortality rates in people with HF are similar to those with common forms of cancer. Clinical guidelines highlight the need for more detailed prognostic information to optimise treatment and care planning for people with HF. Besides proven prognostic biomarkers and numerous newly developed prognostic models for HF clinical outcomes, no risk stratification models have been adequately established. Through a number of linked systematic reviews, we aim to assess the quality of the existing models with biomarkers in HF and summarise the evidence they present. Methods We will search MEDLINE, EMBASE, Web of Science Core Collection, and the prognostic studies database maintained by the Cochrane Prognosis Methods Group combining sensitive published search filters, with no language restriction, from 1990 onwards. Independent pairs of reviewers will screen and extract data. Eligible studies will be those developing, validating, or updating any prognostic model with biomarkers for clinical outcomes in adults with any type of HF. Data will be extracted using a piloted form that combines published good practice guidelines for critical appraisal, data extraction, and risk of bias assessment of prediction modelling studies. Missing information on predictive performance measures will be sought by contacting authors or estimated from available information when possible. If sufficient high quality and homogeneous data are available, we will meta-analyse the predictive performance of identified models. Sources of between-study heterogeneity will be explored through meta-regression using pre-defined study-level covariates. Results will be reported narratively if study quality is deemed to be low or if the between-study heterogeneity is high. Sensitivity analyses for risk of bias impact will be performed. Discussion This project aims to appraise and summarise the methodological conduct and predictive performance of existing clinically homogeneous HF prognostic models in separate systematic reviews. Registration: PROSPERO registration number CRD42019086990

Abstract Background The pneumonia severity index (PSI) and the CURB-65 (confusion, urea, respiratory rate, blood pressure, age ≥ 65 years) score have been shown to predict mortality in community-acquired pneumonia. Their ability to predict influenza-related pneumonia, however, is less well-established. Methods A total of 693 laboratory-confirmed FluA-p patients diagnosed between Jan 2013 and Dec 2018 and recruited from five teaching hospitals in China were included in the study. The sample included 494 patients in the derivation cohort and 199 patients in the validation cohort. The prediction rule was established based on independent risk factors for 30-day mortality in FluA-p patients from the derivation cohort. Results The 30-day mortality of FluA-p patients was 19.6% (136/693). The FluA-p score was based on a multivariate logistic regression model designed to predict mortality. Results indicated the following significant predictors (regression statistics and point contributions toward total score in parentheses): blood urea nitrogen > 7 mmol/L (OR 1.604, 95% CI 1.150–4.492, p = 0.040; 1 points), pO2/FiO2 ≤ 250 mmHg (OR 2.649, 95% CI 1.103–5.142, p = 0.022; 2 points), cardiovascular disease (OR 3.967, 95% CI 1.269–7.322, p

Background: The need for invasive mechanical ventilation (IMV) is linked to significant morbidity and mortality in patients with influenza-related pneumonia (Flu-p). We aimed to develop an assessment tool to predict IMV among Flu-p patients within 14 days of admission.Methods: In total, 1107 Flu-p patients from five teaching hospitals were retrospectively enrolled from January 2012 to December 2019, including 895 patients in the derivation cohort and 212 patients in the validation cohort. The predictive model was established based on independent risk factors for IMV in the Flu-p patients from the derivation cohort.Results: Overall, 10.6% (117/1107) of patients underwent IMV within 14 days of admission. Multivariate regression analyses revealed that the following factors were associated with IMV: early neuraminidase inhibitor use (- 3 points), lymphocytes < 0.8 × 109/L (1 point), multi-lobar infiltrates (1 point), systemic corticosteroid use (1 point), age ≥ 65 years old (1 points), PaO2/FiO2 < 300 mmHg (2 points), respiratory rate ≥ 30 breaths/min (3 points), and arterial PH < 7.35 (4 points). A total score of five points was used to identify patients at risk of IMV. This model had a sensitivity of 85.5%, a specificity of 88.8%, and exhibited better predictive performance than the ROX index (AUROC = 0.909 vs. 0.594, p = 0.004), modified ROX index (AUROC = 0.909 vs. 0.633, p = 0.012), and HACOR scale (AUROC = 0.909 vs. 0.622, p < 0.001) using the validation cohort.Conclusions: Flu-IV score is a valuable prediction rule for 14-day IMV rates in Flu-p patients. However, it should be validated in a prospective study before implementation. [ABSTRACT FROM AUTHOR]

Abstract Background To investigate the frequency of achievement of inactive disease (ID) in children with juvenile idiopathic arthritis (JIA) treated with methotrexate (MTX) as the sole disease-modifyng antirheumatic (DMARD) therapy and to develop a prediction model for lack of attainment of ID. Methods The clinical charts of consecutive patients started with MTX as the sole DMARD between 2000 and 2013 were reviewed. Patient follow-up was censored at first episode of ID or, in case ID was not reached, at last follow-up visit or when a biologic DMARD was prescribed. The characteristic at MTX start of patients who achieved or did not achieve ID were compared with univariate and multivariable analyses. Regression coefficients (β) of variables that entered the best-fitting logistic regression model were converted and summed to obtain a “prediction score” for lack of achievement of ID. Results A total of 375 patients were included in the study. During MTX administration, 8.8% were given systemic corticosteroids and 44.1% intra-articular corticosteroids. After MTX start, 229 (61%) patients achieved ID after a median of 1.7 years, whereas 146 patients (39%) did not reach ID after a median of 1.2 years. On multivariable analysis, independent correlations with lack of achievement of ID were identified for the disease categories of systemic arthritis, enthesitis-related arthritis (ERA) and polyarthritis and C-reactive protein (CRP) > 1.4 mg/dl. The prediction score ranged from 0 to 3 and its cutoff that discriminated best between patients who achieved or did not achieve ID was > 0.5. The categories of systemic arthritis or ERA, both of which had a score greater than 0.5, were sufficient alone to predict a lower likelihood to reach ID. Polyarthritis and increased CRP, whose score was 0.5, assumed a predictive value only when present in association. Conclusion A conventional treatment regimen based on MTX as the sole DMARD led to achievement of ID in a sizeable proportion of children with JIA. Our findings help to outline the characteristics of patients who may deserve a synthetic DMARD other than MTX or the introduction of a biologic DMARD from disease outset.

Background and Aims: Standard treatment for naïve hereditary hemochromatosis patients consists of phlebotomy or a personalized erythrocytapheresis. Erythrocytapheresis is more efficient, but infrequently used because of perceived costs and specialized equipment being needed. The main aim of our study was to develop a model that predicts the number of initial treatment procedures for both treatment methods. This information may help the clinician to select the optimal treatment modality for the individual patient. Methods: We analyzed retrospective data of 125 newly diagnosed patients (C282Y homozygous), treated either with phlebotomy (n = 54) or erythrocytapheresis (n = 71) until serum ferritin (SF) reached levels ≤100 μg/L. To estimate the required number of treatment procedures multiple linear regression analysis was used for each treatment method separately. Results: The linear regression model with the best predictive quality (R2 = 0.74 and 0.73 for erythrocytapheresis and phlebotomy respectively) included initial SF, initial hemoglobin (Hb) level, age, and BMI, where initial SF was independently related to the total number of treatment procedures for both treatment methods. The prediction error expressed in RMSPE and RMSDR was lower for erythrocytapheresis than for phlebotomy (3.8 and 4.1 vs 7.0 and 8.0 respectively), Conclusions: Although the prediction error of the developed model was relatively large, the model may help the clinician to choose the most optimal treatment method for an individual patient. Generally erythrocytapheresis halves the number of treatment procedures for all patients, where the largest reduction (between 55% and 64%) is reached in patients with an initial Hb level ≥ 9 mmol/L (14.5 g/dL). ClinicalTrials.gov number NCT00202436. [ABSTRACT FROM AUTHOR]

PurposeCurrent guidelines recommend prophylactic use of granulocyte-colony stimulating factors (G-CSF) when febrile neutropenia (FN) risk is greater than 20%. Advanced age is a risk factor for FN; however, little is known about the impact of other factors on the incidence of FN in an older population.Patients and methodsWe analyzed SEER-Medicare data (1994-2005) to develop and validate a prediction model for hospitalization with fever, infection, or neutropenia occurring after chemotherapy initiation for patients with breast, colorectal, prostate, and lung cancer.ResultsIn multivariate analysis (N = 58,053) independent predictors of FN included advanced stage at diagnosis [stage 2 (OR 1.29; 95% CI: 1.09-1.53), stage 3 (1.38; 95% CI: 1.19-1.60), and stage 4 (1.57; 95% CI: 1.35-1.83)], number of associated comorbid conditions [one condition (1.13; 95% CI: 1.02-1.28), two conditions (1.39; 95% CI: 1.22-1.57), and three or more conditions (1.81; 95% CI: 1.61-2.04)], receipt of myelosuppressive chemotherapy (1.11; 95% CI: 0.94-1.32), and receipt of chemotherapy within 1 month of diagnosis [1 to 3 months (0.70; 95% CI: 0.62-0.80) and greater than 3 months (0.63; 95% CI: 0.55-0.73)].ConclusionWe created a prediction model for febrile neutropenia with first cycle of chemotherapy in a large population of elderly patients with common malignancies.

Abstract Background Conducting prospective epidemiological studies of hospitalized patients with rare diseases like primary subarachnoid hemorrhage (pSAH) are difficult due to time and budgetary constraints. Routinely collected administrative data could remove these barriers. We derived and validated 3 algorithms to identify hospitalized patients with a high probability of pSAH using administrative data. We aim to externally validate their performance in four hospitals across Canada. Methods Eligible patients include those ≥18 years of age admitted to these centres from January 1, 2012 to December 31, 2013. We will include patients whose discharge abstracts contain predictive variables identified in the models (ICD-10-CA diagnostic codes I60** (subarachnoid hemorrhage), I61** (intracranial hemorrhage), 162** (other nontrauma intracranial hemorrhage), I67** (other cerebrovascular disease), S06** (intracranial injury), G97 (other postprocedural nervous system disorder) and CCI procedural codes 1JW51 (occlusion of intracranial vessels), 1JE51 (carotid artery inclusion), 3JW10 (intracranial vessel imaging), 3FY20 (CT scan (soft tissue of neck)), and 3OT20 (CT scan (abdominal cavity)). The algorithms will be applied to each patient and the diagnosis confirmed via chart review. We will assess each model’s sensitivity, specificity, negative and positive predictive value across the sites. Discussion Validating the Ottawa SAH Prediction Algorithms will provide a way to accurately identify large SAH cohorts, thereby furthering research and altering care.

Abstract Background As complete reporting is essential to judge the validity and applicability of multivariable prediction models, a guideline for the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) was introduced. We assessed the completeness of reporting of prediction model studies published just before the introduction of the TRIPOD statement, to refine and tailor its implementation strategy. Methods Within each of 37 clinical domains, 10 journals with the highest journal impact factor were selected. A PubMed search was performed to identify prediction model studies published before the launch of TRIPOD in these journals (May 2014). Eligible publications reported on the development or external validation of a multivariable prediction model (either diagnostic or prognostic) or on the incremental value of adding a predictor to an existing model. Results We included 146 publications (84% prognostic), from which we assessed 170 models: 73 (43%) on model development, 43 (25%) on external validation, 33 (19%) on incremental value, and 21 (12%) on combined development and external validation of the same model. Overall, publications adhered to a median of 44% (25th–75th percentile 35–52%) of TRIPOD items, with 44% (35–53%) for prognostic and 41% (34–48%) for diagnostic models. TRIPOD items that were completely reported for less than 25% of the models concerned abstract (2%), title (5%), blinding of predictor assessment (6%), comparison of development and validation data (11%), model updating (14%), model performance (14%), model specification (17%), characteristics of participants (21%), model performance measures (methods) (21%), and model-building procedures (24%). Most often reported were TRIPOD items regarding overall interpretation (96%), source of data (95%), and risk groups (90%). Conclusions More than half of the items considered essential for transparent reporting were not fully addressed in publications of multivariable prediction model studies. Essential information for using a model in individual risk prediction, i.e. model specifications and model performance, was incomplete for more than 80% of the models. Items that require improved reporting are title, abstract, and model-building procedures, as they are crucial for identification and external validation of prediction models.

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Background: Respiratory viruses are important etiologies of community-acquired pneumonia. However, current knowledge on the prognosis of respiratory virus-related pneumonia (RV-p) is limited. Thus, here we aimed to establish a clinical predictive model for mortality of patients with RV-p. Methods: A total of 1431 laboratory-confirmed patients with RV-p, including 1169 and 262 patients from respective derivation and validation cohorts from five teaching hospitals in China were assessed between January 2010 and December 2019. A prediction rule was established on the basis of risk factors for 30-day mortality of patients with RV-p from the derivation cohort using a multivariate logistic regression model. Results: The 30-day mortality of patients with RV-p was 16.8% (241/1431). The RV-p score was composed of nine predictors (including respective points of mortality risk): (a) age ⩾65 years (1 point); (b) chronic obstructive pulmonary disease (1 point); (c) mental confusion (1 point); (d) blood urea nitrogen (1 point); (e) cardiovascular disease (2 points); (f) smoking history (2 points); (g) arterial pressure of oxygen/fraction of inspiration oxygen (PaO2/FiO2) < 250 mmHg (2 points); (h) lymphocyte counts <0.8 × 109/L (2 points); (i) arterial PH < 7.35 (3 points). A total of six points was used as the cut-off value for mortality risk stratification. Our model showed a sensitivity of 0.831 and a specificity of 0.783. The area under the receiver operating characteristic curve was more prominent for RV-p scoring [0.867, 95% confidence interval (CI)0.846–0.886] when compared with both pneumonia severity index risk (0.595, 95% CI 0.566–0.624, p < 0.001) and CURB-65 scoring (0.739, 95% CI 0.713–0.765, p < 0.001). Conclusion: RV-p scoring was able to provide a good predictive accuracy for 30-day mortality, which accounted for a more effective stratification of patients with RV-p into relevant risk categories and, consequently, help physicians to make more rational clinical decisions. The reviews of this paper are available via the supplemental material section. [ABSTRACT FROM AUTHOR]

Background: The pneumonia severity index (PSI) and the CURB-65 (confusion, urea, respiratory rate, blood pressure, age ≥ 65 years) score have been shown to predict mortality in community-acquired pneumonia. Their ability to predict influenza-related pneumonia, however, is less well-established.Methods: A total of 693 laboratory-confirmed FluA-p patients diagnosed between Jan 2013 and Dec 2018 and recruited from five teaching hospitals in China were included in the study. The sample included 494 patients in the derivation cohort and 199 patients in the validation cohort. The prediction rule was established based on independent risk factors for 30-day mortality in FluA-p patients from the derivation cohort.Results: The 30-day mortality of FluA-p patients was 19.6% (136/693). The FluA-p score was based on a multivariate logistic regression model designed to predict mortality. Results indicated the following significant predictors (regression statistics and point contributions toward total score in parentheses): blood urea nitrogen > 7 mmol/L (OR 1.604, 95% CI 1.150-4.492, p = 0.040; 1 points), pO2/FiO2 ≤ 250 mmHg (OR 2.649, 95% CI 1.103-5.142, p = 0.022; 2 points), cardiovascular disease (OR 3.967, 95% CI 1.269-7.322, p < 0.001; 3 points), arterial PH < 7.35 (OR 3.959, 95% CI 1.393-7.332, p < 0.001; 3 points), smoking history (OR 5.176, 95% CI 2.604-11.838, p = 0.001; 4 points), lymphocytes < 0.8 × 109/L (OR 8.391, 95% CI 3.271-16.212, p < 0.001; 5 points), and early neurominidase inhibitor therapy (OR 0.567, 95% CI 0.202-0.833, p = 0.005; - 2 points). Seven points was used as the cut-off value for mortality risk stratification. The model showed a sensitivity of 0.941, a specificity of 0.762, and overall better predictive performance than the PSI risk class (AUROC = 0.908 vs 0.560, p < 0.001) and the CURB-65 score (AUROC = 0.908 vs 0.777, p < 0.001).Conclusions: Our results showed that a FluA-p score was easy to derive and that it served as a reliable prediction rule for 30-day mortality in FluA-p patients. The score could also effectively stratify FluA-p patients into relevant risk categories and thereby help treatment providers to make more rational clinical decisions. [ABSTRACT FROM AUTHOR]

As part of the treatment of pelvic fracture, major hemorrhage poses a challenge for trauma surgeons. The aim of the present study was to evaluate the clinical outcomes of blood transfusion in the initial 6 h after pelvic fracture, and to define the blood transfusion volume required for each pelvic fracture type. A retrospective cohort study was performed on patients with pelvic fracture at a single Level I Trauma Centre over a 3-year period. A total of 1,297 patients were transported to our trauma centre within 2 h of injury and blood transfusion was administered in the initial 6 h after pelvic fracture. Review of the patients' medical records provided the initial pelvic radiographs and data from emergency department care. Clinical outcomes, including frequency of blood transfusion, blood transfusion volume, injury severity scores and mortality, were evaluated. All pelvic fractures were defined as closed fractures and patients were categorized according to the Arbeit fuer Osteosynthese (AO) classification system. Statistical methods were used to identify trends to provide guidance for clinical prediction. Complete data were available for 497 patients with pelvic fracture, 104 (20.9%) of which received blood transfusion. The blood transfusion volume in the initial 6 h ranged from 0 to 10,000 ml, with a mean of 1,213.94±1354.11 ml. The total mortality rate was 1.8%. Among the patients with C-type pelvic fractures, the frequency of blood transfusion was 59.0% and the mean volume was 2,191.30±1,740.93 ml. The mortality rate for C-type pelvic fractures was 11.43%. The B3 subtype of pelvic fractures had the highest transfusion frequency (53.6%), while the C3 subtype had the largest blood transfusion volume (5,700.00±4,666.90 ml). Patients with type A-C pelvic fractures had a progressively larger mean transfusion volume, transfusion frequency and mortality in the initial 6 h after pelvic fracture. The AO classification system was demonstrated to be a useful tool for the identification of pelvic fracture risk in the present study. [ABSTRACT FROM AUTHOR]

Edsel B Ing,1 Gabriela Lahaie Luna,2 Andrew Toren,3 Royce Ing,4 John J Chen,5 Nitika Arora,6 Nurhan Torun,7 Otana A Jakpor,8 J Alexander Fraser,9 Felix J Tyndel,10 Arun NE Sundaram,10 Xinyang Liu,11 Cindy TY Lam,1 Vivek Patel,12 Ezekiel Weis,13 David Jordan,14 Steven Gilberg,14 Christian Pagnoux,15 Martin ten Hove21Department of Ophthalmology and Vision Sciences, University of Toronto Medical School, Toronto, 2Department of Ophthalmology, Queen’s University, Kingston, ON, 3Department of Ophthalmology, University of Laval, Quebec, QC, 4Toronto Eyelid, Strabismus and Orbit Surgery Clinic, Toronto, ON, Canada; 5Mayo Clinic, Department of Ophthalmology and Neurology, 6Mayo Clinic, Department of Ophthalmology, Rochester, MN, 7Department of Surgery, Division of Ophthalmology, Harvard Medical School, Boston, MA, 8Harvard Medical School, Boston, MA, USA; 9Department of Clinical Neurological Sciences and Ophthalmology, Western University, London, 10Department of Medicine, University of Toronto Medical School, Toronto, ON, Canada; 11Department of Medicine, Fudan University Shanghai Medical College, Shanghai, People’s Republic of China; 12Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 13Departments of Ophthalmology, Universities of Alberta and Calgary, Edmonton and Calgary, AB, 14Department of Ophthalmology, University of Ottawa, Ottawa, ON, 15Vasculitis Clinic, Mount Sinai Hospital, Toronto, ON, CanadaPurpose: To develop and validate a diagnostic prediction model for patients with suspected giant cell arteritis (GCA).Methods: A retrospective review of records of consecutive adult patients undergoing temporal artery biopsy (TABx) for suspected GCA was conducted at seven university centers. The pathologic diagnosis was considered the final diagnosis. The predictor variables were age, gender, new onset headache, clinical temporal artery abnormality, jaw claudication, ischemic vision loss (VL), diplopia, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and platelet level. Multiple imputation was performed for missing data. Logistic regression was used to compare our models with the non-histologic American College of Rheumatology (ACR) GCA classification criteria. Internal validation was performed with 10-fold cross validation and bootstrap techniques. External validation was performed by geographic site.Results: There were 530 complete TABx records: 397 were negative and 133 positive for GCA. Age, jaw claudication, VL, platelets, and log CRP were statistically significant predictors of positive TABx, whereas ESR, gender, headache, and temporal artery abnormality were not. The parsimonious model had a cross-validated bootstrap area under the receiver operating characteristic curve (AUROC) of 0.810 (95% CI =0.766–0.854), geographic external validation AUROC’s in the range of 0.75–0.85, calibration pH–L of 0.812, sensitivity of 43.6%, and specificity of 95.2%, which outperformed the ACR criteria.Conclusion: Our prediction rule with calculator and nomogram aids in the triage of patients with suspected GCA and may decrease the need for TABx in select low-score at-risk subjects. However, misclassification remains a concern.Keywords: temporal artery biopsy, diagnosis, prediction rule, nomogram, giant cell arteritis, validation

ObjectiveTo identify modifiable predictors of functional decline among community-residing older women and to derive and validate a clinical prediction tool for functional decline based only on modifiable predictors.DesignA prospective cohort study.SettingFour geographic areas of the United States.ParticipantsCommunity-residing women older than age 65 recruited from population-based listings between 1986 and 1988 (n = 6632).MeasurementsModifiable predictors were considered to be those that a clinician seeing an older patient for the first time could reasonably expect to change over a 4-year period: benzodiazepine use, depression, low exercise level, low social functioning, body-mass index, poor visual acuity, low bone mineral density, slow gait, and weak grip. Known predictors of functional decline unlikely to be amenable to intervention included age, education, medical comorbidity, cognitive function, smoking history, and presence of previous spine fracture. All variables were measured at baseline; only modifiable predictors were candidates for the prediction tool. Functional decline was defined as loss of ability over the 4-year interval to perform one or more of five vigorous or eight basic daily activities.ResultsSlow gait, short-acting benzodiazepine use, depression, low exercise level, and obesity were significant modifiable predictors of functional decline in both vigorous and basic activities. Weak grip predicted functional decline in vigorous activities, whereas long-acting benzodiazepine use and poor visual acuity predicted functional decline in basic activities. A prediction rule based on these eight modifiable predictors classified women in the derivation set into three risk groups for decline in vigorous activities (12%, 25%, and 39% risk) and two risk groups for decline in basic activities (2% and 10% risk). In the validation set, the probabilities of functional decline were nearly identical.ConclusionsA substantial portion of the variation of functional decline can be attributed to risk factors amenable to intervention over the short term. Using eight modifiable predictors that can be identified in a single office visit, clinicians can identify older women at risk for functional decline.

Background: The specificity of the leukocyte esterase test (87%) is suboptimal. The objective of this study was to identify more specific screening tests that could reduce the number of children who unnecessarily receive antimicrobials to treat a presumed urinary tract infection (UTI). Methods: Prospective cross-sectional study to compare inflammatory proteins in blood and urine samples collected at the time of a presumptive diagnosis of UTI. We also evaluated serum RNA expression in a subset. Results: We enrolled 200 children; of these, 89 were later demonstrated not to have a UTI based on the results of the urine culture obtained. Urinary proteins that best discriminated between children with UTI and no UTI were involved in T cell response proliferation (IL-9, IL-2), chemoattractants (CXCL12, CXCL1, CXCL8), the cytokine/interferon pathway (IL-13, IL-2, INFγ), or involved in innate immunity (NGAL). The predictive power (as measured by the area under the curve) of a combination of four urinary markers (IL-2, IL-9, IL-8, and NGAL) was 0.94. Genes in the pathways related to inflammation were also upregulated in serum of children with UTI. Conclusions: Urinary proteins involved in the inflammatory response may be useful in identifying children with false positive results with current screening tests for UTI; this may reduce unnecessary treatment. [ABSTRACT FROM AUTHOR]

Study Design: A prospective observational study.Objective: To externally validate the prediction model developed by Schellingerhout and colleagues predicting global perceived effect at 12 weeks in patients with neck pain and to update and internally validate the updated model.Summary Of Background Data: Only one prediction model for neck pain has undergone some external validation with good promise. However, the model needs testing in other populations before implementation in clinical practice.Methods: Patients with neck pain (n = 773) consulting Norwegian chiropractors were followed for 12 weeks. Parameters from the original prediction model were applied to this sample for external validation. Subsequently, two random samples were drawn from the full study sample. One sample (n = 436) was used to update the model; by recalibration, removing noninformative covariates, and adding new possible predictors. The updated model was tested in the other sample (n = 303) using stepwise logistic regression analysis. Main outcomes for performance of models were discrimination and calibration plots.Results: Three hundred seventy patients (47%) in the full study sample reported persistent pain at 12 weeks. The performance of the original model was poor, area under the receiver operating characteristics curve was 0.55 with a Confidence Interval of 0.51-0.59. The updated model included Radiating pain to shoulder and/or elbow, education level, physical activity, consultation-type (first- time, follow-up or maintenance consultation), expected course of neck pain, previous course of neck pain, number of pain sites, and the interaction term Physical activity##Number of pain sites. The area under the receiver operating characteristics curve was 0.65 with a 95% Confidence Interval of 0.58-0.71 for the updated model.Conclusion: The predictive accuracy of the original model performed insufficiently in the sample of patients from Norwegian chiropractors and the model is therefore not recommended for that setting. Only one predictor from the original model was retained in the updated model, which demonstrated reasonable good performance predicting outcome at 12 weeks. Before considering clinical use, a new external validation is required.Level Of Evidence: 3. [ABSTRACT FROM AUTHOR]

Introduction: Patients with short bowel syndrome (SBS) are dependent on parenteral nutrition (PN) while their bowel attempts to compensate. Our objective was to create an SBS disease severity score to predict the probability of achieving enteral autonomy (EA).Methods: A retrospective cohort study of children with SBS managed by our Intestinal Rehabilitation Program was completed. Data abstracted included demographic, bowel anatomy, and outcome variables including conjugated bilirubin (CB) and enteral nutrition (EN) tolerated 6 months postoperatively. Univariate analysis and Cox proportional hazards (CPH) model were performed. A score was created based on weighting of coefficients. An α-value of < 0.05 was considered significant.Results: One hundred thirty-nine patients were analyzed (61% males). Ninety-five (68%) achieved EA. Patients possessing >50% residual small bowel (hazard ratio [HR] 2.68 [95% confidence interval {CI} 1.60-4.49], P < 0.001), ileocecal valve intact (HR 0.61 [95% CI 0.37-1.01], P < 0.055), and >50% enteral tolerance at 6 months (HR 5.70 [95% CI 2.77-11.74] P < 0.001) were positively associated with EA. CB >34 µmol/L (2 mg/dL) was negatively associated with EA (HR 0.42 [95% CI0.27-0.66], P < 0.001). A severity score was created by weighting CPH parameter estimates (small bowel length >50%, ileocecal valve intact, CB <34 µmol/L, and EN >50% for a maximum score of 8), and disease severity strata were developed (severe [25.7% EA], moderate [52.9% EA], and mild [97.1% EA]).Conclusion: We propose a pediatric SBS disease severity score that predicts probability of EA. The score allows prognostication of individual patients and could assist research by adjusting outcome reporting or stratifying recruitment. [ABSTRACT FROM AUTHOR]

Ideally, prediction rules should be published in such a way that readers may apply them, for example, to make predictions for their own data. While this is straightforward for simple prediction rules, such as those based on the logistic regression model, this is much more difficult for complex prediction rules derived by machine learning tools. We conducted a survey of articles reporting prediction rules that were constructed using the random forest algorithm and published in PLOS ONE in 2014–2015 in the field "medical and health sciences", with the aim of identifying issues related to their applicability. Making a prediction rule reproducible is a possible way to ensure that it is applicable; thus reproducibility is also examined in our survey. The presented prediction rules were applicable in only 2 of 30 identified papers, while for further eight prediction rules it was possible to obtain the necessary information by contacting the authors. Various problems, such as nonresponse of the authors, hampered the applicability of prediction rules in the other cases. Based on our experiences from this illustrative survey, we formulate a set of recommendations for authors who aim to make complex prediction rules applicable for readers. All data including the description of the considered studies and analysis codes are available as supplementary materials. [ABSTRACT FROM AUTHOR]

Background: An alcohol text message intervention recently demonstrated effects in reducing heavy episodic drinking (HED) days at the three month follow-up in young adults with a history of hazardous drinking. An important next step in understanding intervention effects involves identifying baseline participant characteristics that predict who will benefit from intervention exposure to support clinical decision-making and guide further intervention development. To identify baseline characteristics that predict HED, this exploratory study used a prediction rule ensemble (PRE). Compared to more complex decision-tree methods (e.g., random forest), PREs have comparable performance, while generating simpler rules that can directly identify subgroups that do or do not respond to intervention., Methods: This secondary analysis examined data from 916 young adults who reported HED (68.5% female, mean age = 22.1, SD = 2.1), were enrolled in an alcohol text message randomized clinical trial and who completed baseline assessment and the three month follow-up. A PRE with ten fold cross-validation, which included 21 baseline variables representing sociodemographic characteristics (e.g., sex, age, race, ethnicity, college enrollment), alcohol consumption (frequency of alcohol consumption, quantity consumed on a typical drinking day, frequency of HED), impulsivity subscales (i.e., negative urgency, positive urgency, lack of premeditation, lack of perseverance, sensation seeking), readiness to change, perceived peer drinking and HED-related consequences, and intervention status were used to predict HED at the three month follow-up., Results: The PRE identified 12 rules that predicted HED at three months ( R 2 = 0.23) using 7 baseline features. Only two cases (0.2%) were not classified by the 12 rules. The most important features for predicting three month HED included baseline alcohol consumption, negative urgency score, and perceived peer drinking., Conclusions: The rules provide interpretable decision-making tools that predict who has higher alcohol consumption following exposure to alcohol text message interventions using baseline participant characteristics (prior to intervention), which highlight the importance of interventions related to negative urgency and peer alcohol use., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The prevalence of methicillin-resistant Staphylococcus aureus (MRSA) colonization among nursing home residents is high. Health-care workers (HCWs) often serve as a vector in MRSA transmission. The ability to identify residents who are likely to transmit MRSA to HCWs' hands and clothing during clinical care is important so that infection control measures, such as Contact Precautions, can be employed. Using data on demographic and clinical characteristics collected from residents of community nursing homes in Maryland and Michigan between 2012 and 2014, we developed a clinical prediction rule predicting the probability of MRSA transmission to HCWs' gowns. We externally validated this model in a cohort of Department of Veterans Affairs nursing home residents from 7 states between 2012 and 2016. The prediction model, which included sex, race, resident dependency on HCWs for care, the presence of any medical device, diabetes mellitus, and chronic skin breakdown, showed good performance (C statistic = 0.70; sensitivity = 76%, specificity = 49%) in the development set. The decision curve analysis indicated that this model has greater clinical utility than use of a nares surveillance culture for MRSA colonization, which is current clinical practice for placing hospital inpatients on Contact Precautions. The prediction rule demonstrated less utility in the validation cohort, suggesting that a separate rule should be developed for residents of Veterans Affairs nursing homes. [ABSTRACT FROM AUTHOR]

Purpose: Increasing prevalence of candidemia in Internal Medicine wards (IMWs) has been reported in recent years, but risk factors for candida bloodstream infection in patients admitted to IMW may differ from those known in other settings. The aim of this study was to identify risk factors and define a prediction rule for the early recognition of the risk of candidemia in IMW inpatients.Methods: This was a multicentric, retrospective, observational case-control study on non-neutropenic patients with candidemia admitted to IMWs of four large Italian Hospitals. Each eligible patient with candidemia (case) was matched to a control with bacteremia. Stepwise logistic regression analyses were performed.Results: Overall, 300 patients (150 cases and 150 controls) were enrolled. The following factors were associated with an increased risk of candidemia and weighted to build a score: total parenteral nutrition (OR 2.45, p = 0.008; 1 point); central venous catheter (OR 2.19, p = 0.031; 1 point); peripherally inserted central catheter (OR 5.63, p < 0.0001; 3 points), antibiotic treatment prior (OR 2.06; p = 0.059; 1 point) and during hospitalization (OR2.38, p = 0.033; 1 point); neurological disability (OR 2.25, p = 0.01; 1 point); and previous hospitalization within 3 months (OR 1.56, p = 0.163; 1 point). At ROC curve analysis, a final score ≥ 4 showed 84% sensitivity, 76% specificity, and 80% accuracy in predicting the risk of candidemia.Conclusions: The proposed scoring system showed to be a simple and highly performing tool in distinguishing bloodstream infections due to Candida and bacteria in patients admitted to IMW. The proposed rule might help to reduce delay in empirical treatment and improve appropriateness in antifungal prescription in septic patients. [ABSTRACT FROM AUTHOR]

Study Objective: Prior studies have identified risk factors for recurrent Clostridium difficile infection (CDI), but few studies have integrated these factors into a clinical prediction rule that can aid clinical decision‐making. The objectives of this study were to derive and validate a CDI recurrence prediction rule to identify patients at risk for first recurrence in a national cohort of veterans. Design: Retrospective cohort study. Data Source: Veterans Affairs Informatics and Computing Infrastructure. Patients: A total of 22,615 adult Veterans Health Administration beneficiaries with first‐episode CDI between October 1, 2002, and September 30, 2014; of these patients, 7538 were assigned to the derivation cohort and 15,077 to the validation cohort. Measurements and Main Results: A 60‐day CDI recurrence prediction rule was created in a derivation cohort using backward logistic regression. Those variables significant at p<0.01 were assigned an integer score proportional to the regression coefficient. The model was then validated in the derivation cohort and a separate validation cohort. Patients were then split into three risk categories, and rates of recurrence were described for each category. The CDI recurrence prediction rule included the following predictor variables with their respective point values: prior third‐ and fourth‐generation cephalosporins (1 point), prior proton pump inhibitors (1 point), prior antidiarrheals (1 point), nonsevere CDI (2 points), and community‐onset CDI (3 points). In the derivation cohort, the 60‐day CDI recurrence risk for each score ranged from 7.5% (0 points) to 57.9% (8 points). The risk score was strongly correlated with recurrence (R2 = 0.94). Patients were split into low‐risk (0–2 points), medium‐risk (3–5 points), and high‐risk (6–8 points) classes and had the following recurrence rates: 8.9%, 20.2%, and 35.0%, respectively. Findings were similar in the validation cohort. Conclusion: Several CDI and patient‐specific factors were independently associated with 60‐day CDI recurrence risk. When integrated into a clinical prediction rule, higher risk scores and risk classes were strongly correlated with CDI recurrence. This clinical prediction rule can be used by providers to identify patients at high risk for CDI recurrence and help guide preventive strategy decisions, while accounting for clinical judgment. [ABSTRACT FROM AUTHOR]