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.

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.

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.

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.

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.

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.

A Clinical Decision Tool for Risk Stratifying Patients with Systemic Sclerosis-Related Pulmonary Hypertension.

We devised a scoring system to identify patients with systemic sclerosis (SSc) at risk for pulmonary hypertension (PH) and predict all-cause mortality. Using 7 variables obtained via pulmonary function testing, echocardiography, and computed tomographic chest imaging, we applied the score to a retrospective cohort of 117 patients with SSc. There were 60 (51.3%) who were diagnosed with PH by right heart catheterization. Using a scoring threshold ≥ 0, our decision tool predicted PH with a sensitivity, specificity, and accuracy of 0.87 (95% CI 0.75, 0.94), 0.74 (95% CI 0.60, 0.84), and 0.80 (95% CI 0.72, 0.87), respectively. When adjusted for age at PH diagnosis, sex, and receipt of pulmonary arterial vasodilators, each one-point score increase was associated with an adjusted HR of 1.19 (95% CI 1.05, 1.34) for all-cause mortality. With further validation in external cohorts, our simplified clinical decision tool may better streamline earlier detection of PH in SSc.

Clinical predictors of pulmonary embolism for inpatients: are computed tomography pulmonary angiograms being requested appropriately?

BACKGROUND: The heterogeneity of inpatient pulmonary embolism (PE) presentations may lead to computed tomography pulmonary angiograms (CTPA) being over-requested. Current clinical predictors for PE, including Wells criteria and Pulmonary Embolism Rule-out Criteria (PERC), have predominantly focussed on outpatient and emergency department populations. AIM: To determine the clinical indicators for ordering inpatient CTPA and the predictors of positive scans for PE. METHODS: Consecutive inpatient CTPA (performed >24 h after admission) from January 2017 to December 2017 were retrospectively reviewed. Variables including baseline characteristics, vital signs and risk factors for PE were extracted. RESULTS: A total of 312 CTPA was reviewed (average patient age 67 years; 46% male) and 36 CTPA were positive for PE (11.5%). The average time to inpatient CTPA request was 7 days. Clinical indicators associated with positive scans were hypoxia (odds ratio (OR) 2.4; 95% confidence interval (CI) 1.1-5.6), tachypnoea (OR 2.5; 95% CI 1.2-6.0), recent surgery or immobilisation (OR 2.7; 95% CI 1.2-6.4), S1Q3T3 pattern on electrocardiogram (ECG; OR 7.2; 95% CI 1.4-35.7) and right bundle branch block pattern on ECG (OR 4.7; 95% CI 1.6-13.1). Hypotension, fever and malignancy were not significant. Both PERC and Wells criteria had poor positive predictive value (12% and 27% respectively), but the negative predictive value for PERC and Wells was 100% and 95.8% respectively. CONCLUSION: Inpatient CTPA appear to be over-requested and can potentially be rationalised based on a combination of clinical predictors and Wells criteria and/or PERC rule. Further prospective studies are needed to develop accurate clinical decision tools targeted towards inpatients.

A Simplified Clinical Prediction Rule for Prognosticating Dependent Daily Living in Patients with Thoracic Spinal Cord Injury: A Multicenter Nationwide Japan Registry Study.

INTRODUCTION: Clinical prediction rule (CPR) using decision tree analysis is able to show the branching of the variables under consideration in a clear, hierarchical manner, including specific reference values, which can be used as classifiers in clinical practice. However, CPRs developed by decision tree analysis for predicting the degree of independent living of patients with thoracic spinal cord injury (SCI) are few. The purpose of this study was to develop a simplified CPR for prognosticating dependent daily living in patients with thoracic SCI. METHODS: We extracted data on patients with thoracic SCI from a national multicenter registry database, the Japan Rehabilitation Database (JRD). All patients with thoracic SCI who were hospitalized within 30 days after the injury onset were included. The independent living was categorized in the JRD as follows: independent socially, independent at home, needing care at home, independent at the facility, and needing care at the facility. These categories were used as the objective variables in classification and regression tree (CART) analysis. The CART algorithm was applied to develop the CPR for predicting whether patients with thoracic SCI achieve independent living at hospital discharge. RESULTS: Three hundred ten patients with thoracic SCI were included in the CART analysis. The CART model identified, in a hierarchical order, patient's age, residual function level, and the bathing sub-score of Functional Independence Measure as the top three factors with moderate classification accuracy and area under the curve. CONCLUSIONS: We developed a simplified, moderately accurate CPR for predicting whether patients with thoracic SCI achieve independent living at hospital discharge.

Clinical prediction rule for identifying older patients with toxigenic clostridioides difficile at the time of hospital admission.

BACKGROUND: This study aimed to develop and validate a clinical prediction rule to screen older patients at risk of being toxigenic Clostridioides difficile carriers at the time of hospital admission. METHODS: This retrospective case-control study was performed at a university-affiliated hospital. Active surveillance using a real-time polymerase chain reaction (PCR) assay for the toxin genes of C. difficile was conducted among older patients (≥ 65 years) upon admission to the Division of Infectious Diseases of our institution. This rule was drawn from a derivative cohort between October 2019 and April 2021 using a multivariable logistic regression model. Clinical predictability was evaluated in the validation cohort between May 2021 and October 2021. RESULTS: Of 628 PCR screenings for toxigenic C. difficile carriage, 101 (16.1%) yielded positive findings. To establish clinical prediction rules in the derivation cohort, the formula was derived using significant predictors for toxigenic C. difficile carriage at admission, such as septic shock, connective tissue diseases, anemia, recent use of antibiotics, and recent use of proton-pump inhibitors. In the validation cohort, the sensitivity, specificity, and positive and negative predictive values of the prediction rule, based on a cut-off value of ≥ 0.45, were 78.3%, 70.8%, 29.5%, and 95.4%, respectively. CONCLUSION: This clinical prediction rule for identifying toxigenic C. difficile carriage at admission may facilitate the selective screening of high-risk groups. To implement it in a clinical setting, more patients from other medical institutions need to be prospectively examined.

Development of a clinical prediction rule for mobility status at discharge in patients with total knee arthroplasty: Using a decision tree model.

BACKGROUND: Total knee arthroplasty (TKA) is an effective treatment to improve mobility in patients with severe knee osteoarthritis. However, some patients continue to have poor mobility after surgery. The preoperative identification of patients with poor mobility after TKA allows for better treatment selection and appropriate goal setting. The purpose of this study was to develop a clinical prediction rule (CPR) to predict mobility after TKA. METHODS: This study included patients undergoing primary TKA. Predictors of outcome included patient characteristics, physical function, and psychological factors, which were measured preoperatively. The outcome measure was the Timed Up and Go test, which was measured at discharge. Patients with a score of ≥11 s were considered having a low-level of mobility. The classification and regression tree methodology of decision tree analysis was used for developing a CPR. RESULTS: Of the 101 cases (mean age, 72.2 years; 71.3 % female), 26 (25.7 %) were classified as low-mobility. Predictors were the modified Gait Efficacy Scale, age, knee pain on the operated side, knee extension range of motion on the non-operated side, and Somatic Focus, a subscale of the Tampa Scale for Kinesiophobia (short version). The model had a sensitivity of 50.0 %, a specificity of 98.7 %, a positive predictive value of 92.9 %, a positive likelihood ratio of 37.5, and an area under the receiver operating characteristic curve of 0.853. CONCLUSION: We have developed a CPR that, with some accuracy, predicts the mobility outcomes of patients after TKA. This CPR may be useful for predicting postoperative mobility and clinical goal setting.

External Validation of the COVID-NoLab and COVID-SimpleLab Prognostic Tools.

Our objective was to externally validate 2 simple risk scores for mortality among a mostly inpatient population with COVID-19 in Canada (588 patients for COVID-NoLab and 479 patients for COVID-SimpleLab). The mortality rates in the low-, moderate-, and high-risk groups for COVID-NoLab were 1.1%, 9.6%, and 21.2%, respectively. The mortality rates for COVID-SimpleLab were 0.0%, 9.8%, and 20.0%, respectively. These values were similar to those in the original derivation cohort. The 2 simple risk scores, now successfully externally validated, offer clinicians a reliable way to quickly identify low-risk inpatients who could potentially be managed as outpatients in the event of a bed shortage. Both are available online (https://ebell-projects.shinyapps.io/covid_nolab/ and https://ebell-projects.shinyapps.io/COVID-SimpleLab/).

Prediction of 2-year major adverse cardiac events from myocardial perfusion scintigraphy and clinical risk factors.

BACKGROUND: We developed CRAX2MACE, a new tool derived from clinical and SPECT myocardial perfusion imaging (MPI) variables, to predict 2-year probability of major adverse cardiac event (MACE) comprising death, hospitalized acute myocardial infarction or coronary revascularization. METHODS: Consecutive individuals with SPECT MPI 2001-2008 had two-year MACE determined from population-based health services data. CRAX2MACE included age, sex, diabetes, recent cardiac hospitalization, pharmacologic stress, stress total perfusion deficit (TPD), ischemic (stress-rest) TPD, left ventricular ejection fraction and transient ischemic dilation ratio. Two-year event rates were classified as low (< 5%), moderate (5.0-9.9%), high (10-19.9%) and very high (20% or greater). RESULTS: The study population comprised 3896 individuals for the development and 1946 for the validation subgroups with subsequent MACE in 589 (15.1%) and 272 (14.0%), respectively. CRAX2MACE, derived from the development subgroups, accurately stratified MACE risk in the validation subgroup (area under the receiver operating characteristics curve 0.79) with stepwise increase in the observed event rate with increasing predicted risk category (low, 2.3%; moderate, 5.5%; high, 18.8%; very high 33.2%; P-trend < 0.001). CONCLUSIONS: A simple tool based upon clinical risk factors and MPI variables predicts 2-year cardiac events. Risk stratification between the low and very high groups was greater than tenfold.

A bacteraemia risk prediction model: development and validation in an emergency medicine population.

OBJECTIVE: Design a risk model to predict bacteraemia in patients attended in emergency departments (ED) for an episode of infection. METHODS: This was a national, prospective, multicentre, observational cohort study of blood cultures (BC) collected from adult patients (≥ 18 years) attended in 71 Spanish EDs from October 1 2019 to March 31, 2020. Variables with a p value < 0.05 were introduced in the univariate analysis together with those of clinical significance. The final selection of variables for the scoring scale was made by logistic regression with selection by introduction. The results obtained were internally validated by dividing the sample in a derivation and a validation cohort. RESULTS: A total of 4,439 infectious episodes were included. Of these, 899 (20.25%) were considered as true bacteraemia. A predictive model for bacteraemia was defined with seven variables according to the Bacteraemia Prediction Model of the INFURG-SEMES group (MPB-INFURG-SEMES). The model achieved an area under the curve-receiver operating curve of 0.924 (CI 95%:0.914-0.934) in the derivation cohort, and 0.926 (CI 95%: 0.910-0.942) in the validation cohort. Patients were then split into ten risk categories, and had the following rates of risk: 0.2%(0 points), 0.4%(1 point), 0.9%(2 points), 1.8%(3 points), 4.7%(4 points), 19.1% (5 points), 39.1% (6 points), 56.8% (7 points), 71.1% (8 points), 82.7% (9 points) and 90.1% (10 points). Findings were similar in the validation cohort. The cut-off point of five points provided the best precision with a sensitivity of 95.94%, specificity of 76.28%, positive predictive value of 53.63% and negative predictive value of 98.50%. CONCLUSION: The MPB-INFURG-SEMES model may be useful for the stratification of risk of bacteraemia in adult patients with infection in EDs, together with clinical judgement and other variables independent of the process and the patient.

Validating a clinical prediction score for Legionella-related community acquired pneumonia.

BACKGROUND: Legionella-related community acquired pneumonia (CAP) is a disease with an increasing incidence and a high mortality rate, especially if empirical antibiotic therapy is inadequate. Antibiotic treatment highly relies on clinical symptoms, although proven non-specific, because currently available diagnostic techniques provide insufficient accuracy for detecting Legionella CAP on admission. This study validates a diagnostic scoring system for detection of Legionella-related CAP, based on six items on admission (Legionella prediction score). METHODS: We included patients with Legionella-related CAP admitted to five large Dutch hospitals between 2006 and 2016. Controls were non-Legionella-related CAP patients. The following six conditions were rewarded one point if present: fever > 39.4 °C; dry cough; hyponatremia (sodium) < 133 mmol/L; lactate dehydrogenase (LDH) > 225 mmol/L; C-reactive protein (CRP) > 187 mg/L and platelet count < 171 × 109/L. The accuracy of the prediction score was assessed by calculating the area under the curve (AUC) through logistic regression analysis. RESULTS: We included 131 cases and 160 controls. A score of 0 occurred in non-Legionella-related CAP patients only, a score of 5 and 6 in Legionella-related CAP patients only. A cut-off ≥ 4 resulted in a sensitivity of 58.8% and a specificity of 93.1%. The AUC was 0.89 (95% CI 0.86-0.93). The strongest predictors were elevated LDH, elevated CRP and hyponatremia. CONCLUSIONS: This multi-centre study validates the Legionella prediction score, an easily applicable diagnostic scoring system, in a large group of patients and finds high diagnostic accuracy. The score shows promise for future prospective validation and could contribute to targeted antibiotic treatment of suspected Legionella CAP.

Development of a Clinical Prediction Rule to Identify Physical Activity After Total Hip Arthroplasty.

OBJECTIVE: To develop clinical prediction rule (CPR) of physical activity 1 year after total hip arthroplasty (THA). DESIGN: Retrospective cohort study. SETTING: University hospital with orthopedic surgery. PARTICIPANTS: The study group included 321 patients (56 men) who underwent primary THA (N=321). INTERVENTION: Not applicable. MAIN OUTCOMES MEASURES: The data collected included age, body mass index, clinical score from the questionnaires, hip pain, range of motion, muscle strength, and Physical functions (10-meter walk test [10MWT], timed Up and Go test, sit-to-stand test). Patients were classified into sufficient and insufficient activity groups based on their University of California, Los Angeles (UCLA) activity score 1 year after THA. Variables measured preoperatively and 3 weeks postoperatively were analyzed using univariate and multivariate methods to derive CPR for physical activity. RESULTS: A CPR was developed using the following 5 factors and cutoffs: age 70.5 years or younger, preoperative UCLA activity score ≥3.5, preoperative hip abduction strength ≥0.54 Nm/kg, preoperative knee extension strength ≥1.04 Nm/kg, and 10MWT ≤8.49 seconds 3 weeks after surgery. The presence of 4 of the 5 factors predicted a sufficient physical activity level at 1 year, with a positive likelihood ratio of 5.94 and probability of 85.4%. The presence of 5 predictor variables increased the probability of sufficient physical activity after THA to 94.7%. CONCLUSIONS: This study developed a CPR for physical activity 1 year after THA. Having 4 or more of the 5 measurements were useful indicators for predicting of physical activity 1 year postoperatively.

Temporal Validation of an Assessment Tool that Predicts a Possibility of Home Discharge for Patients with Acute Stroke.

OBJECTIVES: Several prediction models have been developed to assess discharge destinations for patients with acute stroke; however, few studies have performed external validation. We aimed to perform a temporal external validation of a prediction tool to identify stroke patients with a high possibility of discharge to home. MATERIALS AND METHODS: From December 2017 to July 2019, consecutive patients with acute stroke were included. Clinical nurses and physical therapists applied the prediction model to assess the patients' possibility of home discharge. Whether or not the patient was discharged their own home was the outcome measured. We calculated the sensitivity and specificity of the model and evaluated the discrimination and calibration based on the area under the curve (AUC) and the calibration plot. RESULTS: Of the 1214 patients assessed, 618 (51%) were discharged home. Using the same cutoff values recommended in the study that first described the tool, we determined the sensitivity and specificity of 91% and 59%, respectively. The AUC to assess the model discrimination was 0.80 (95% confidence interval, 0.77-0.82) and the calibration plot showed acceptable agreement between the predicted and observed outcomes. CONCLUSIONS: The tool showed a high sensitivity, as expected, in the present study, which examined external validity during the different study periods.

Developing a Clinical Prediction Rule for Gait Independence at Discharge in Patients with Stroke: A Decision-Tree Algorithm Analysis.

OBJECTIVES: To develop a clinical prediction rule (CPR) for gait independence at discharge in patients with stroke, using the decision-tree algorithm and to investigate the usefulness of CPR at admission to the rehabilitation ward. MATERIALS AND METHODS: We included 181 subjects with stroke during the postacute phase. The Chi-squared automatic interaction detection analysis method with 10-fold cross-validation was used to develop two CPRs; CPR 1 using easily obtainable data available at admission; CPR 2 using easily obtainable data available 1 month after admission, for prediction of gait independence at discharge. RESULTS: The degree of independence of toileting was extracted as a first node in the development of two CPRs to predict gait independence at discharge. CPR 1 included the presence of delirium. CPR 2 included problem-solving abilities. The accuracy and area under the curve of CPR 1 were 84.5% and 0.911, respectively; those of CPR 2 were 89.0% and 0.958, respectively. CONCLUSIONS: Toileting independence is a key factor in predicting the gait independence for the discharge of patients with stroke during the postacute phase. Early intervention, during the acute phase, for delirium and cognitive decline, as well as for toileting, increases the possibility of gait independence at discharge.

Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb.

BACKGROUND: The number of studies on the characteristics of patients with stroke who would benefit from robot-assisted upper limb rehabilitation is limited, and there are no clear criteria for determining which individuals should receive such treatment. The current study aimed to develop a clinical prediction rule using machine learning to identify the characteristics of patients with stroke who can the achieve minimal clinically important difference of the Fugl-Meyer Upper Extremity Evaluation (FMA-UE) after single-joint hybrid assistive limb (HAL-SJ) rehabilitation. METHODS: This study included 71 patients with subacute stroke who received HAL-SJ rehabilitation. The chi-square automatic interaction detector (CHAID) model was applied to predict improvement in upper limb motor function. Based the analysis using CHAID, age, sex, days from stroke onset to the initiation of HAL-SJ rehabilitation, and upper limb motor and cognitive functions were used as independent variables. Improvement in upper limb motor function was determined based on the minimal clinically important difference of the FMA-UE, which was used as a dependent variable. RESULTS: According to the CHAID model, the FMA-UE score during the initiation of HAL-SJ rehabilitation was the most significant predictive factor for patients who are likely to respond to the intervention. Interestingly, this therapy was more effective in patients with moderate upper limb motor dysfunction and early initiation of HAL-SJ rehabilitation. The accuracy of the CHAID model was 0.89 (95% confidence interval: 0.81-0.96). CONCLUSION: We developed a clinical prediction rule for identifying the characteristics of patients with stroke whose upper limb motor function can improve with HAL-SJ rehabilitation.

The Effects of Using a Clinical Prediction Rule to Prioritize Diagnostic Testing on Transmission and Hospital Burden: A Modeling Example of Early Severe Acute Respiratory Syndrome Coronavirus 2.

BACKGROUND: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. METHODS: Using early severe acute respiratory syndrome coronavirus disease 2 (SARS-CoV-2) as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. RESULTS: We found that applying this CPR (area under the curve, 0.69; 95% confidence interval, .68-.70) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (ie, "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. In addition, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit burden. CONCLUSION: We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.SummaryWhen the demand for diagnostic tests exceeds capacity, the use of a clinical prediction rule to prioritize diagnostic testing can have meaningful impact on population-level outcomes, including delaying and lowering the infection peak, and reducing healthcare burden.