Development of a new definition of maternal near miss based on organ dysfunction in Latin America and the Caribbean: A prospective multicenter cohort study.

BACKGROUND: There has been debate over whether the existing World Health Organization (WHO) criteria accurately represent the severity of maternal near misses. OBJECTIVE: This study assessed the diagnostic accuracy of two WHO clinical and laboratory organ dysfunction markers for determining the best cutoff values in a Latin American setting. METHODS: A prospective multicenter cohort study was conducted in five Latin American countries. Patients with severe maternal complications were followed up from admission to discharge. Organ dysfunction was determined using clinical and laboratory data, and participants were classified according to severe maternal outcomes. This study compares the diagnostic criteria of Latin American Centre for Perinatology, Network for Adverse Maternal Outcomes (CLAP/NAMO) to WHO standards. RESULTS: Of the 698 women studied, 15.2% had severe maternal outcomes. Most measured variables showed significant differences between individuals with and without severe outcomes (all P-values <0.05). Alternative cutoff values suggested by CLAP/NAMOs include pH ≤7.40, lactate ≥2.3 mmol/L, respiratory rate ≥ 24 bpm, oxygen saturation ≤ 96%, PaO2/FiO2 ≤ 342 mmHg, platelet count ≤189 × 109 × mm3, serum creatinine ≥0.8 mg/dL, and total bilirubin ≥0.67 mg/dL. No significant differences were found when comparing the diagnostic performance of the CLAP/NAMO criteria to that of the WHO standards. CONCLUSION: The CLAP/NAMO values were comparable to the WHO maternal near-miss criteria, indicating that the WHO standards might not be superior in this population. These findings suggest that maternal near-miss thresholds can be adapted regionally, improving the identification and management of severe maternal complications in Latin America.

Development and validation of a clinical prediction rule for walking independence in hospitalized older adults with a vertebral compression fracture.

OBJECTIVE: No reports on factors or Clinical prediction rules (CPRs) associated with walking independence among patients with vertebral compression fractures (VCFs) are available. Evidence regarding epidemiological walking independence rates is also sparse. Here, we sought to (i) obtain epidemiological data on the probability of inpatients with VCFs achieving walking independence, and (ii) develop and validate a CPR to determine walking independence in hospitalized patients with VCFs. PATIENTS AND METHODS: We conducted a retrospective cross-sectional observational study of patients aged ≥60 years who were hospitalized for VCF at four hospitals in Japan in 2019-2022. The outcome was walking independence at discharge. We performed a binomial logistic regression analysis to assess predictors of walking independence. Five independent variables were entered: age, American Society of Anesthesiologists physical status, cognitive function, Berg Balance Scale (BBS), and 10-m walking test. Among the independent variables that were significant, we converted the continuous variables to binary data by calculating cut-off values and then created the CPR. The area under the curve (AUC) was calculated as the measure of the CPR's diagnostic accuracy, and internal validation was conducted by bootstrapping. RESULTS: Of the 240 patients, 188 (78.3%) achieved walking independence. Cognitive function and the BBS score (with a cut-off of 45 points) were identified as significant predictors. We created a CPR using these two items (0-2 points). The CPR's AUC was 0.92 (0.874-0.967), and internal validation by bootstrapping yielded a mean AUC of 0.919 with a slope of 0.965. CONCLUSION: The walking independence rate of patients with a VCF during hospitalization was 78.3%, with cognitive function and BBS being predictors. The developed CPR performed well enough to retrospectively predict walking independence in VCF patients. The BBS cut-off value and the CPR may serve as useful indicators for clinicians to predict VCF patients' walking independence.

Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department.

BACKGROUND: This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores (EWSs) and three shock indices in early sepsis prediction in the emergency department (ED). METHODS: We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong. The primary outcome was sepsis (Sepsis-3 definition) within 48 h of ED presentation. Using c-statistics and the DeLong test, we compared 11 EWSs, including the National Early Warning Score 2 (NEWS2), Modified Early Warning Score, and Worthing Physiological Scoring System (WPS), etc., and three shock indices (the shock index [SI], modified shock index [MSI], and diastolic shock index [DSI]), with Systemic Inflammatory Response Syndrome (SIRS) and quick Sequential Organ Failure Assessment (qSOFA) in predicting the primary outcome, intensive care unit admission, and mortality at different time points. RESULTS: We analyzed 601 patients, of whom 166 (27.6%) developed sepsis. NEWS2 had the highest point estimate (area under the receiver operating characteristic curve [AUROC] 0.75, 95%CI 0.70-0.79) and was significantly better than SIRS, qSOFA, other EWSs and shock indices, except WPS, at predicting the primary outcome. However, the pooled sensitivity and specificity of NEWS2 ≥ 5 for the prediction of sepsis were 0.45 (95%CI 0.37-0.52) and 0.88 (95%CI 0.85-0.91), respectively. The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point. CONCLUSION: NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.

Framework for personalized prediction of treatment response in relapsing-remitting multiple sclerosis: a replication study in independent data.

BACKGROUND: Individualizing and optimizing treatment of relapsing-remitting multiple sclerosis patients is a challenging problem, which would benefit from a clinically valid decision support. Stühler et al. presented black box models for this aim which were developed and internally evaluated in a German registry but lacked external validation. METHODS: In patients from the French OFSEP registry, we independently built and validated models predicting being free of relapse and free of confirmed disability progression (CDP), following the methodological roadmap and predictors reported by Stühler. Hierarchical Bayesian models were fit to predict the outcomes under 6 disease-modifying treatments given the individual disease course up to the moment of treatment change. Data was temporally split on 2017, and models were developed in patients treated earlier (n = 5517). Calibration curves, discrimination, mean squared error (MSE) and relative percentage of root MSE (RMSE%) were assessed by external validation of models in more-recent patients (n = 3768). Non-Bayesian fixed-effects GLMs were also applied and their outcomes were compared to these of the Bayesian ones. For both, we modelled the number of on-therapy relapses with a negative binomial distribution, and CDP occurrence with a binomial distribution. RESULTS: The performance of our temporally-validated relapse model (MSE: 0.326, C-Index: 0.639) is potentially superior to that of Stühler's (MSE: 0.784, C-index: 0.608). Calibration plots revealed miscalibration. Our CDP model (MSE: 0.072, C-Index: 0.777) was also better than its counterpart (MSE: 0.131, C-index: 0.554). Results from non-Bayesian fixed-effects GLM models were similar to the Bayesian ones. CONCLUSIONS: The relapse and CDP models rebuilt and externally validated in independent data could compare and strengthen the credibility of the Stühler models. Their model-building strategy was replicable.

Assessing acutely ill children in general practice using the National PEWS and LqSOFA clinical scores: a retrospective cohort study.

BACKGROUND: Clinical tools are needed in general practice to help identify seriously ill children. The Liverpool quick Sequential Organ Failure Assessment (LqSOFA) was validated in an Emergency Department and performed well. The National Paediatric Early Warning score (PEWS) has been introduced in hospitals throughout England with hopes for implementation in general practice. AIM: To validate the LqSOFA and National PEWS in general practice. DESIGN/SETTING: Secondary analysis of 6,703 children <5 years presenting to 225 general practices in England and Wales with acute illnesses, linked to hospital data. METHOD: Variables from the LqSOFA and National PEWS were mapped onto study data to calculate score totals. A primary outcome of admission within two days of GP consultation was used to calculate sensitivity, specificity, negative predictive values (NPV), positive predictive values (PPV) and area-under-the-curve (AUC). RESULTS: 104/6,703 children were hospitalised within two days (pre-test probability 1.6%). The sensitivity of the LqSOFA was 30.6% (95% confidence interval 21.8% - 41.0%), with a specificity of 84.7% (83.7% - 85.6%), PPV of 3.0% (2.1% - 4.4%), NPV of 98.7% (98.4% - 99.0%), and AUC of 0.58 (0.53 - 0.63). The sensitivity of the National PEWS was 81.0% (71.0% - 88.1%), with a specificity of 32.5% (31.2% - 33.8%); PPV of 1.9% (1.5% - 2.5%); NPV of 99.1% (98.4% - 99.4%) and AUC of 0.66 (0.59 - 0.72). CONCLUSION: Although the NPVs appear useful, due to low pre-test probabilities rather than discriminative ability, neither tool accurately identified hospitalisations. Unconsidered use by GPs could result in unsustainable referrals.

Prospective Validation of a Simple Risk Score to Predict Hospitalization during the Omicron Phase of COVID-19.

INTRODUCTION: We previously developed a simple risk score with 3 items (age, patient report of dyspnea, and any relevant comorbidity), and in this report validate it in a prospective sample of patients, stratified by vaccination status. METHODS: Data were abstracted from a structured electronic health record of primary care and urgent care 8 patients with COVID-19 in the Lehigh Valley Health Network from 11/21/2021 and 10/31/2022 9 (Omicron variant). Our previously derived risk score was calculated for each of 19,456 patients, 10 and the likelihood of hospitalization was determined. Area under the ROC curve was calculated. RESULTS: We were able to place 13,239 patients (68%) in a low-risk group with only a 0.16% risk of 13 hospitalization. The moderate risk group with 5622 patients had a 2.2% risk of hospitalization 14 and might benefit from close outpatient follow-up, whereas the high-risk group with only 574 15 patients (2.9% of all patients) had an 8.9% risk of hospitalization and may require further 16 evaluation. Area under the curve was 0.844. DISCUSSION: We prospectively validated a simple risk score for primary and urgent care patients with COVID1919 that can support outpatient triage decisions around COVID-19.

Temporal validation of a clinical prediction rule for distinguishing locomotive syndromes in community-dwelling older adults: A cross-sectional study from the DETECt-L study.

Objectives: Clinical prediction rules are used to discriminate patients with locomotive syndrome and may enable early detection. This study aimed to validate the clinical predictive rules for locomotive syndrome in community-dwelling older adults. Methods: We assessed the clinical prediction rules for locomotive syndrome in a cross-sectional setting. The age, sex, and body mass index of participants were recorded. Five physical function tests-grip strength, single-leg standing time, timed up-and-go test, and preferred and maximum walking speeds-were measured as predictive factors. Three previously developed clinical prediction models for determining the severity of locomotive syndrome were assessed using a decision tree analysis. To assess validity, the sensitivity, specificity, likelihood ratio, and post-test probability of the clinical prediction rules were calculated using receiver operating characteristic curve analysis for each model. Results: Overall, 280 older adults were included (240 women; mean age, 74.8 ± 5.2 years), and 232 (82.9%), 68 (24.3%), and 28 (10.0%) participants had locomotive syndrome stages ≥ 1, ≥ 2, and = 3, respectively. The areas under the receiver operating characteristics curves were 0.701, 0.709, and 0.603, in models 1, 2, and 3, respectively. The accuracies of models 1 and 2 were moderate. Conclusions: These findings indicate that the models are reliable for community-dwelling older adults.

Telephone triage of chest pain in out-of-hours primary care: external validation of a symptom-based prediction rule to rule out acute coronary syndromes.

INTRODUCTION: Telephone triage is pivotal for evaluating the urgency of patient care, and in the Netherlands, the Netherlands Triage Standard (NTS) demonstrates moderate discrimination for chest pain. To address this, the Safety First Prediction Rule (SFPR) was developed to improve the safety of ruling out acute coronary syndrome (ACS) during telephone triage. METHODS: We conducted an external validation of the SFPR using data from the TRACE study, a retrospective cohort study in out-of-hours primary care. We evaluated the diagnostic accuracy assessment for ACS, major adverse cardiovascular events (MACE), and major events within 6 weeks. Moreover, we compared its performance with that of the NTS algorithm. RESULTS: Among 1404 included patients (57.3% female, 6.8% ACS, 8.6% MACE), the SFPR demonstrated good discrimination for ACS (C-statistic: 0.79; 95%-CI: 0.75-0.83) and MACE (C-statistic: 0.79; 95%-CI: 0.0.76-0.82). Calibration was satisfactory, with overestimation observed in high-risk patients for ACS. The SFPR (risk threshold 2.5%) trended toward higher sensitivity (95.8% vs. 86.3%) and negative predictive value (99.3% vs. 97.6%) with a lower negative likelihood ratio (0.10 vs. 0.34) than the NTS algorithm. CONCLUSION: The SFPR proved robust for risk stratification in patients with acute chest pain seeking out-of-hours primary care in the Netherlands. Further prospective validation and implementation are warranted to refine and establish the rule's clinical utility.

Validation of the PediBIRN-7 clinical prediction rule for pediatric abusive head trauma.

BACKGROUND: The PediBIRN-7 clinical prediction rule incorporates the (positive or negative) predictive contributions of completed abuse evaluations to estimate abusive head trauma (AHT) probability after abuse evaluation. Applying definitional criteria as proxies for AHT and non-AHT ground truth, it performed with sensitivity 0.73 (95 % CI: 0.66-0.79), specificity 0.87 (95 % CI: 0.82-0.90), and ROC-AUC 0.88 (95 % CI: 0.85-0.92) in its derivation study. OBJECTIVE: To validate the PediBIRN-7's AHT prediction performance in a novel, equivalent, patient population. PARTICIPANTS AND SETTINGS: Consecutive, acutely head-injured children <3 years hospitalized for intensive care across eight sites between 2017 and 2020 with completed skeletal surveys and retinal exams (N = 342). METHODS: Secondary analysis of an existing, cross-sectional, prospective dataset, including assignment of patient-specific estimates of AHT probability, calculation of AHT prediction performance measures (ROC-AUC, sensitivity, specificity, predictive values), and completion of sensitivity analyses to estimate best- and worst-case prediction performances. RESULTS: Applying the same definitional criteria, the PediBIRN-7 performed with sensitivity 0.74 (95 % CI: 0.66-0.81), specificity 0.77 (95 % CI: 0.70-0.83), and ROC-AUC 0.83 (95 % CI: 0.78-0.88). The reduction in ROC-AUC was statistically insignificant (p = .07). Applying physicians' final consensus diagnoses as proxies for AHT and non-AHT ground truth, the PediBIRN-7 performed with sensitivity 0.73 (95 % CI: 0.66-0.79), specificity 0.87 (95 % CI: 0.82-0.90), and ROC-AUC 0.90 (95 % CI: 0.87-0.94). Sensitivity analyses demonstrated minimal changes in rule performance. CONCLUSION: The PediBIRN-7's overall AHT prediction performance has been validated in a novel, equivalent, patient population. Its patient-specific estimates of AHT probability can inform physicians' AHT-related diagnostic reasoning after abuse evaluation.

The role of echocardiography in pulmonary embolism for the prediction of in-hospital mortality: a retrospective study.

PURPOSE: Pulmonary Embolism (PE) is the third leading cause of cardiovascular death, following myocardial infarction and stroke. The latest European Society of Cardiology (ESC) guidelines on PE recommend short-term prognostic stratification based on right ventricular (RV) overload detected by transthoracic echocardiography (TTE) or contrast-enhanced chest CT. The aim of the study is to find out which of the signs of right ventricular dysfunction best predicts in-hospital mortality (IHM). METHODS: This is a monocentric, retrospective study including adult patients admitted from the emergency department with a c-e cCT confirmed diagnosis of PE between January 2018 and December 2022 who underwent a TTE within 48 h. RESULTS: 509 patients (median age 76 years [IQR 67-84]) were included, with 7.1% IHM. At univariate analysis, RV/LV ratio > 1 (OR 2.23, 95% CI 1.1-4.5), TAPSE < 17 mm (OR 4.73, 95% CI 2.3-9.8), the D-shape (OR 3.73, 95% CI 1.71-8.14), and LVEF < 35% (OR 5.78, 95% CI 1.72-19.47) resulted significantly correlated with IHM. However, at multivariate analysis including also haemodynamic instability, PESI class > II, and abnormal hs-cTnI levels, only LVEF < 35% (OR 5.46, 95% CI 1.32-22.61) resulted an independent predictor of IHM. CONCLUSION: Despite the recognised role of TTE in the early management of patients with circulatory shock and suspected PE, signs of RV dysfunction have been shown to be poor predictors of IHM, whereas severely reduced LVEF is an independent risk factor for in-hospital death.

[Usefulness of the MPB-INFURG-SEMES model to predict bacteremia in the patient with solid tumor in the Emergency Department]. / Utilidad del modelo MPB-INFURG-SEMES para predecir bacteriemia en el paciente con neoplasia sólida en el Servicio de Urgencias.

OBJECTIVE: To analyse a new risk score to predict bacteremia (MPB-INFURG-SEMES) in the patients with solid tumor attender for infection in the emergency departments (ED). METHODS: Prospective, multicenter observational cohort study of blood cultures (BC) obtained from adult patients with solid neoplasia treated in 63 EDs for infection from November 1, 2019, to March 31, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The prognostic performance for true bacteremia was calculated with the chosen cut-off for getting the sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: A total of 857 blood samples wered cultured. True cases of bacteremia were confirmed in 196 (22.9%). The remaining 661 cultures (77.1%) wered negative. And, 42 (4.9%) were judged to be contaminated. The model's area under the receiver operating characteristic curve was 0.923 (95% CI,0.896-0.950). The prognostic performance with a model's cut-off value of ≥ 5 points achieved 95.74% (95% CI, 94,92-96.56) sensitivity, 76.06% (95% CI, 75.24-76.88) specificity, 53.42%(95% CI, 52.60-54.24) positive predictive value and 98.48% (95% CI, 97.66- 99.30) negative predictive value. CONCLUSIONS: The MPB-INFURG-SEMES score is useful for predicting bacteremia in the adults patients with solid tumor seen in the ED.

Evaluating Clinical Prediction Rules for Bacteremia Detection in the Emergency Department: A Retrospective Review.

BACKGROUND: Bacteremia is a major cause of morbidity. Blood cultures are the gold standard for diagnosing bacteremia. OBJECTIVE: To compare previously published clinical decision rules for predicting a true positive blood culture (bacteremia) in the emergency department. METHODS: Retrospective analysis of medical records of patients who had a blood culture performed in a tertiary hospital emergency department in 2020 (12 months). Positive blood cultures were compared with randomly selected negative blood cultures (1:4 ratio). Blood cultures were analyzed per patient presentation. Clinical data from patient presentations were extracted and appraised against the modified-Shapiro (mShapiro) rule and systemic inflammatory response syndrome (SIRS) criteria to calculate diagnostic accuracy to detect bacteremia. RESULTS: During the study period, 3870 blood cultures were taken from 2921 patients: 476 (12.3%) cultures were positive for bacterial growth, from 421 individual patient presentations (10 excluded as incomplete data). Of included patients, 338 were true positives and 73 contaminates, these were compared with 1446 patients with negative blood culture presentations. Evaluating mShapiro's rule and SIRS criteria to detect bacteremia vs. no bacteremia (negative + contaminated cultures) had a sensitivity of 94.4% (95% confidence interval [CI] 91.4-96.4%) and 84.9% (95% CI 80.7-88.3%), respectively, and a specificity of 37.9% (95% CI 35.5-40.1%) and 33.8% (95% CI 31.5-36.3%), respectively. Both had a high negative predictive value for bacteremia of 96.8% (95% CI 95.1-98.0) and 91.0% (95% CI 88.3-93.1) for mShapiro's rule and SIRS criteria, respectively. CONCLUSIONS: In this cohort, mShapiro's rule performed better than the SIRS criteria at predicting bacteremia.

Prediction of mortality in acute pulmonary embolism in cancer-associated thrombosis (MAUPE-C): derivation and validation of a multivariable model.

Optimal risk stratification of patients with cancer and pulmonary embolism (PE) remains unclear. We constructed a clinical prediction rule (CPR) named 'MAUPE-C' to identify patients with low 30 days mortality. The study retrospectively developed and internally validated a CPR for 30 days mortality in a cohort of patients with cancer and PE (both suspected and unsuspected). Candidate variables were chosen based on the EPIPHANY study, which categorized patients into 3 groups based on symptoms, signs, suspicion and patient setting at PE diagnosis. The performance of 'MAUPE-C' was compared to RIETE and sPESI scores. Univariate analysis confirmed that the presence of symptoms, signs, suspicion and inpatient diagnosis were associated with 30 days mortality. Multivariable logistic regression analysis led to the exclusion of symptoms as predictive variable. 'MAUPE-C' was developed by assigning weights to risk factors related to the ß coefficient, yielding a score range of 0 to 4.5. After receiver operating characteristic (ROC) curve analysis, a cutoff point was established at ≤ 1. Prognostic accuracy was good with an area under the curve (AUC) of 0.77 (95% CI 0.71-0.82), outperforming RIETE and sPESI scores in this cohort (AUC of 0.64 [95% CI 0.57-0.71] and 0.57 [95% CI 0.49-0.65], respectively). Forty-five per cent of patients were classified as low risk and experienced a 2.79% 30 days mortality. MAUPE-C has good prognostic accuracy in identifying patients at low risk of 30 days mortality. This CPR could help physicians select patients for early discharge.

The Kocher-Caird Criteria for Pediatric Septic Arthritis of the Hip: Time for a Change in the Kingella Era?

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.

Prospective external validation of the FluScore risk score for influenza in outpatients.

BACKGROUND: Testing for influenza in patients with acute lower respiratory tract infection (LRTI) is common and in some cases is performed for all patients with LRTI. A more selective approach to testing could be more efficient. METHODS: We used data from two prospective studies in the US primary and urgent care settings that enrolled patients with acute LRTI or influenza-like illness. Data were collected in the 2016, 2019, 2021, and 2022 flu seasons. All patients underwent polymerase chain reaction (PCR) testing for influenza and the FluScore was calculated based on patient-reported symptoms at their initial visit. The probability of influenza in each risk group was reported, as well as stratum-specific likelihood ratios (SSLRs) for each risk level. RESULTS: The prevalence of influenza within risk groups varied based on overall differences in flu seasons and populations. However, the FluScore exhibited consistent performance across various seasons and populations based on the SSLRs. The FluScore had a consistent SSLR range of 0.20 to 0.23 for the low-risk group, 0.63 to 0.99 for the moderate-risk group, and 1.46 to 1.67 for the high-risk group. The diagnostic odds ratio based on the midpoints of these ranges was 7.25. CONCLUSIONS: The FluScore could streamline patient categorization, identifying patients who could be exempted from testing, while identifying candidates for rapid influenza tests. This has the potential to be more efficient than a "one size fits all" test strategy, as it strategically targets the use of tests on patients most likely to benefit. It is potentially usable in a telehealth setting.

Prediction Rules Identify Which Young Adults Have Higher Rates of Heavy Episodic Drinking After Exposure to 12-Week Text Message Interventions.

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 (R2 = 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.

Overuse of Computed Tomography Pulmonary Angiography and Low Utilization of Clinical Prediction Rules in Suspected Pulmonary Embolism Patients at a Regional Australian Hospital.

A pulmonary embolism (PE) is an obstruction in the pulmonary arterial system and may include non-specific signs and symptoms. Clinical prediction rules (CPRs) assess the pretest probability (PTP) of a PE to prevent the overuse of computed tomography pulmonary angiography (CTPA). CTPA overuse results in patient harm and health system waste. This study aimed to evaluate CTPA usage in an Australian regional hospital through analyzing CTPA encounters. A retrospective chart analysis was undertaken of 100 CTPAs conducted at an Australian regional hospital from April to May 2023. Analysis was undertaken for parameters including risk factors, signs and symptoms, investigations, and the use of CPRs. Overall, 86% of patients had signs and/or symptoms of a PE within a week of examination, and 6% of the population had signs of deep vein thrombosis. More than half of the population had no risk factors, while the most prevalent risk factors were a recent history of immobilization/trauma and/or having surgery that required general anesthesia in the last 4 weeks. The most common co-morbidity was chronic lung disease (11%). For the pre-test diagnostic workup, the ECG was the most ordered investigation. The Wells' score was used at 10%, while most patients did not have any CPRs applied. The prevalence of PEs discovered on CTPAs was 9%. CPRs were under-utilized in this Australian regional hospital. The D-dimers for ruling out subjects with low PTP derived from CPRs were also underused. This led to the inappropriate overordering of CTPAs, resulting in negative implications for patients and unnecessary costs to the health system.

Clinical prediction rules for multi surfaces walking independence using the Berg Balance Scale or Mini-Balance Evaluation Systems Test in persons with stroke.

BACKGROUND: A Clinical prediction rule (CPR) for determining multi surfaces walking independence in persons with stroke has not been established. OBJECTIVES: To develop a CPR for determining multi surfaces walking independence in persons with stroke. METHODS: This was a multicenter retrospective analysis of 419 persons with stroke. We developed a Berg Balance Scale (BBS)-model CPR combining the BBS, comfortable walking speed (CWS) and cognitive impairment, and a Mini-Balance Evaluation Systems Test (Mini-BESTest)-model CPR combining the Mini-BESTest, CWS, and cognitive impairment. A logistic regression analysis was conducted with multi surfaces walking independence as the dependent variable and each factor as an independent variable. The identified factors were scored (0, 1) based on reported cutoff values. The CPR's accuracy was verified by the area under the curve (AUC). We used a bootstrap method internal validation and calculated the CPR's posttest probability. RESULTS: The logistic regression analysis showed that the BBS, CWS, and cognitive impairment were factors in the BBS model, and the Mini-BESTest was a factor in the Mini-BESTest model. The CPRs were 0-3 points for the BBS model and 0-1 points for the Mini-BESTest model. The AUCs (bootstrap mean AUC) of the CPR score were 0.89 (0.90) for the BBS model and 0.72 (0.72) for the Mini-BESTest model. The negative predictive value (negative likelihood ratio) was 97% (0.054) for CPR scores < 2 for the BBS model and 94% (0.060) for CPR scores < 1 for the Mini-BESTest model. CONCLUSIONS: The CPR developed herein is useful for determining multi surfaces walking independence.

A Single Dermatome Clinical Prediction Rule for Independent Walking 1 Year After Spinal Cord Injury.

OBJECTIVE: To derive and validate a simple, accurate CPR to predict future independent walking ability after SCI at the bedside that does not rely on motor scores and is predictive for those initially classified in the middle of the SCI severity spectrum. DESIGN: Retrospective cohort study. Binary variables were derived, indicating degrees of sensation to evaluate predictive value of pinprick and light touch variables across dermatomes. The optimal single sensory modality and dermatome was used to derive our CPR, which was validated on an independent dataset. SETTING: Analysis of SCI Model Systems dataset. PARTICIPANTS: Individuals with traumatic SCI. The data of 3679 participants (N=3679) were included with 623 participants comprising the derivation dataset and 3056 comprising the validation dataset. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Self-reported ability to walk both indoors and outdoors. RESULTS: Pinprick testing at S1 over lateral heels, within 31 days of SCI, accurately identified future independent walkers 1 year after SCI. Normal pinprick in both lateral heels provided good prognosis, any pinprick sensation in either lateral heel provided fair prognosis, and no sensation provided poor prognosis. This CPR performed satisfactorily in the middle SCI severity subgroup. CONCLUSIONS: In this large multi-site study, we derived and validated a simple, accurate CPR using only pinprick sensory testing at lateral heels that predicts future independent walking after SCI.