Predictors of prolonged supratherapeutic serum lithium concentrations: a retrospective chart review.

INTRODUCTION: The Extracorporeal Treatments in Poisoning (EXTRIP) workgroup suggests hemodialysis in severe lithium poisoning if specific criteria are met. One criterion is if the expected time to obtain a lithium concentration <1.0 mEq/L with optimal management is >36 h. There are a lack of data regarding which patient characteristics are associated with the rate at which patients achieve a lithium concentration <1.0 mEq/L. METHODS: We conducted a retrospective chart review analyzing hospital electronic medical records. Inclusion criteria consisted of a lithium concentration >1.2 mEq/L during hospitalization. We excluded patients who received extracorporeal treatment before 36 h elapsed from time of initial lithium concentration >1.2 mEq/L. The primary analysis consisted of a Cox regression and a secondary analysis evaluated the nomogram method described by Buckley and colleagues for predicting prolonged supratherapeutic lithium concentration. RESULTS: One hundred and one patients were included in the study. The median time to reach a lithium concentration <1.0 mEq/L was 42.5 h (IQR: 33.8-51.1). Older patients, patients taking a thiazide, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, patients with a higher initial lithium concentration, and patients with higher sodium concentrations achieved a lithium concentration <1 mEq/L at a slower rate. For the nomogram analysis, sensitivity (61.5%) and specificity (54.5%) were moderate, the positive predictive value (16.7%) was poor, and the negative predictive value (90.6%) was excellent. DISCUSSION: The results from our primary analysis suggest that identifying higher serum sodium concentration and use of certain antihypertensives that decrease glomerular filtration rate as predictors of an increased time to reach a therapeutic lithium concentration may help identify patients who meet the Extracorporeal Treatments in Poisoning criteria for hemodialysis. The nomogram method performed similarly to prior validation studies. CONCLUSIONS: In this retrospective chart review of patients with supratherapeutic lithium concentrations, we identified several risk factors for prolonged supratherapeutic lithium concentrations.

Continuous Monitoring by a Wearable Sensor Did Not Enhance Discharge Decision-Making in an Acute Admission Ward: Results of a Randomized Controlled Trial.

Adding continuous monitoring to usual care at an acute admission ward did not have an effect on the proportion of patients safely discharged. Implementation challenges of continuous monitoring may have contributed to the lack of effect observed.

Factors predicting long-term outcomes following physiotherapy in patients with subacromial pain syndrome: a secondary analysis.

BACKGROUND: Although patients with shoulder complaints are frequently referred to physiotherapy, putative predictive factors for outcomes are still unclear. In this regard, only a limited amount of scientific data for patients with subacromial pain syndrome exist, with inconsistent results. An improved knowledge about the ability of baseline variables to predict outcomes could help patients make informed treatment decisions, prevent them from receiving ineffective treatments, and minimize the risk of developing chronic pain. AIM: The aims of this secondary longitudinal analysis are threefold: First, to investigate baseline differences between patients with and without successful long-term outcomes following physiotherapy. Second, to compare the predictive ability of two sets of putative predictive variables on outcomes, one based on the literature and one based on the data of the original trial. Third, to explore the contribution of short-term follow-up data to predictive models. METHODS: Differences between responders and nonresponders were calculated. The predictive ability of variables defined through literature and of variables based on the Akaike Information Criterion (AIC) from the original trial dataset on the Shoulder Pain and Disability Index and the Patients' Global Impression of Change at the one-year follow-up were analyzed. To test the robustness of the results, different statistical models were used. To investigate the contribution of follow-up data to prediction, short-term data were included in the analyses. RESULTS: A sample of 87 patients with subacromial pain syndrome was analyzed. 77% (n = 67) of these participants were classified as responders. Higher expectations and short-term change scores were positive, and higher fear avoidance beliefs, greater baseline disability and pain levels were negative predictors of long-term outcomes in patients with subacromial pain syndrome. CONCLUSIONS: Although our results are in line with previous research and support the use of clinical factors for prediction, our findings suggest that psychological factors, especially patient expectations and fear avoidance beliefs, also contribute to long-term outcomes and should therefore be considered in the clinical context and further research. However, the hypotheses and recommendations generated from our results need to be confirmed in further studies due to their explorative nature. TRIAL REGISTRATION: The original trial was registered at Current Controlled Trials under the trial registration number ISRCTN86900354 on March 17, 2010.

Prediction models for diagnosis and prognosis of the colonization or infection of multidrug-resistant organisms in adults: a systematic review, critical appraisal, and meta-analysis.

BACKGROUND: Prediction models help to target patients at risk of multidrug-resistant organism (MDRO) colonization or infection and could serve as tools informing clinical practices to prevent MDRO transmission and inappropriate empiric antibiotic therapy. However, there is limited evidence to identify which among the available models are of low risk of bias and suitable for clinical application. OBJECTIVES: To identify, describe, appraise, and summarise the performance of all prognostic and diagnostic models developed or validated for predicting MDRO colonization or infection. DATA SOURCES: Six electronic literature databases and clinical registration databases were searched until April 2022. STUDY ELIGIBILITY CRITERIA: Development and validation studies of any multivariable prognostic and diagnostic models to predict MDRO colonization or infection in adults. PARTICIPANTS: Adults (≥ 18 years old) without MDRO colonization or infection (in prognostic models) or with unknown or suspected MDRO colonization or infection (in diagnostic models). ASSESSMENT OF RISK OF BIAS: The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias. Evidence certainty was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. METHODS OF DATA SYNTHESIS: Meta-analyses were conducted to summarize the discrimination and calibration of the models' external validations conducted in at least two non-overlapping datasets. RESULTS: We included 162 models (108 studies) developed for diagnosing (n = 135) and predicting (n = 27) MDRO colonization or infection. Models exhibited a high-risk of bias, especially in statistical analysis. High-frequency predictors were age, recent invasive procedures, antibiotic usage, and prior hospitalization. Less than 25% of the models underwent external validations, with only seven by independent teams. Meta-analyses for one diagnostic and two prognostic models only produced very low to low certainty of evidence. CONCLUSIONS: The review comprehensively described the models for identifying patients at risk of MDRO colonization or infection. We cannot recommend which models are ready for application because of the high-risk of bias, limited validations, and low certainty of evidence from meta-analyses, indicating a clear need to improve the conducting and reporting of model development and external validation studies to facilitate clinical application.

RAMA-WeRA Risk Score in Predicting the Ruptured Appendicitis in Emergency Department; a Multicenter Study for External Validation.

Introduction: Distinguishing between ruptured and non-ruptured acute appendicitis presents a significant challenge. This study aimed to validate the accuracy of RAMA-WeRA Risk Score in predicting ruptured appendicitis (RA) in emergency department. Methods: This study was a multicenter diagnostic accuracy study conducted across six hospitals in Thailand from February 1, 2022, to January 20, 2023. The eligibility criteria included individuals aged >15 years suspected of acute appendicitis, presenting to the ED, and having an available pathology report following appendectomy or intraoperative diagnosis by the surgeon. We assessed the screening performance characteristics of RAMA-WeRA Risk Score, in detecting the ruptured appendicitis (RA) cases. Results: 860 patients met the study criteria. 168 (19.38%) had RA and 692 (80.62%) patients had non-RA. The area under the receiver operating characteristic curve (AuROC) of RAMA-WeRA Risk Score was 75.11% (95% CI: 71.10, 79.11). The RAMA-WeRA Risk Score > 6 points (high-risk group) demonstrated a positive likelihood ratio (LR) of 3.22 in detecting the ruptured cases. The sensitivity and specificity of score in > 6 cutoff point was 43.8% (95%CI: 36.2, 51.6) and 86.4% (95%CI: 83.6, 88.9), respectively. Conclusions: The RAMA-WeRA Risk Score can predict rupture in patients presenting with suspected acute appendicitis in the emergency department with total accuracy of 75% for high-risk cases.

Performance of risk scores in predicting major bleeding in left ventricular assist device recipients: a comparative external validation.

Background: Implantation of a left ventricular assist device (LVAD) is a crucial therapeutic option for selected end-stage heart failure patients. However, major bleeding (MB) complications postimplantation are a significant concern. Objectives: We evaluated current risk scores' predictive accuracy for MB in LVAD recipients. Methods: We conducted an observational, single-center study of LVAD recipients (HeartWare or HeartMate-3, November 2010-December 2022) in the Netherlands. The primary outcome was the first post-LVAD MB (according to the International Society on Thrombosis and Haemostasis [ISTH] and Interagency Registry for Mechanically Assisted Circulatory Support [INTERMACS], and INTERMACS combined with intracranial bleeding [INTERMACS+] criteria). Mortality prior to MB was considered a competing event. Discrimination (C-statistic) and calibration were evaluated for the Hypertension, Abnormal Renal/Liver Function, Stroke, Bleeding History or Predisposition, Labile INR, Elderly, Drugs/Alcohol Concomitantly score, Hepatic or Renal Disease, Ethanol Abuse, Malignancy, Older Age, Reduced Platelet Count or Function, Re-Bleeding, Hypertension, Anemia, Genetic Factors, Excessive Fall Risk and Stroke score, Anticoagulation and Risk Factors in Atrial Fibrillation score, Outpatient Bleeding Risk Index, venous thromboembolism score, atrial fibrillation score, and Utah Bleeding Risk Score (UBRS). Results: One hundred four patients were included (median age, 64 years; female, 20.2%; HeartWare, 90.4%; HeartMate-3, 9.6%). The cumulative MB incidence was 75.7% (95% CI 65.5%-85.9%) by ISTH and INTERMACS+ criteria and 67.0% (95% CI 56.0%-78.0%) per INTERMACS criteria over a median event-free follow-up time of 1916 days (range, 59-4521). All scores had poor discriminative ability on their intended prediction timeframe. Cumulative area under the receiving operator characteristic curve ranged from 0.49 (95% CI 0.35-0.63, venous thromboembolism-BLEED) to 0.56 (95% CI 0.47-0.65, UBRS) according to ISTH and INTERMACS+ criteria and from 0.48 (95% CI 0.40-0.56, Anticoagulation and Risk Factors in Atrial Fibrillation) to 0.56 (95% CI 0.47-0.65, UBRS) per INTERMACS criteria. All models showed poor calibration, largely underestimating MB risk. Conclusion: Current bleeding risk scores exhibit inadequate predictive accuracy for LVAD recipients. There is a need for an accurate risk score to identify LVAD patients at high risk of MB who may benefit from patient-tailored antithrombotic therapy.

[Predictive effect of the dual-parametric MRI modified maximum diameter of the lesions with PI-RADS 4 and 5 on the clinically significant prostate cancer].

OBJECTIVE: To assess the rationality of the maximum lesion diameter of 15 mm in prostate imaging reporting and data system (PI-RADS) as a criterion for upgrading a lesion from category 4 to 5 and improve it to enhance the prediction of clinically significant prostate cancer (csPCa). METHODS: In this study, the patients who underwent prostate magnetic resonance imaging (MRI) and prostate biopsy at Peking University First Hospital from 2019 to 2022 as a development cohort, and the patients in 2023 as a validation cohort were reviewed. The localization and maximum diameter of the lesion were fully evaluated. The area under the curve (AUC) and the cut-off value of the maximum diameter of the lesion to predict the detection of csPCa were calculated from the receiver operating characteristics (ROC) curve. Confounding factors were reduced by propensity score matching (PSM). Diagnostic efficacy was compared in the validation cohort. RESULTS: Of the 589 patients in the development cohort, 358 (60.8%) lesions were located in the peripheral zone and 231 (39.2%) were located in the transition zone, and 496 (84.2%) patients detected csPCa. The median diameter of the lesions in the peripheral zone was smaller than that in the transition zone (14 mm vs. 19 mm, P < 0.001). In the ROC analysis of the maximal diameter on the csPCa prediction, there was no statistically significant difference between the peri-pheral zone (AUC=0.709) and the transition zone (AUC=0.673, P=0.585), and the cut-off values were calculated to be 11.5 mm for the peripheral zone and 16.5 mm for the migrating zone. By calcula-ting the Youden index for the cut-off values in the validation cohort, we found that the categorisation by lesion location led to better predictive results. Finally, the net reclassification index (NRI) was 0.170. CONCLUSION: 15 mm as a criterion for upgrading the PI-RADS score from 4 to 5 is reasonable but too general. The cut-off value for peripheral zone lesions is smaller than that in transitional zone. In the future consideration could be given to setting separate cut-off values for lesions in different locations.

Anxiety in young people: Analysis from a machine learning model.

The study addresses the detection of anxiety symptoms in young people using artificial intelligence models. Questionnaires such as the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7) are used to collect data, with a focus on early detection of anxiety. Three machine learning models are employed: Support Vector Machine (SVM), K Nearest Neighbors (KNN), and Random Forest (RF), with cross-validation to assess their effectiveness. Results show that the RF model is the most efficient, with an accuracy of 91 %, surpassing previous studies. Significant predictors of anxiety are identified, such as parental education level, alcohol consumption, and social security affiliation. A relationship is observed between anxiety and personal and family history of mental illness, as well as with characteristics external to the model, such as family and personal history of depression. The analysis of the results highlights the importance of considering not only clinical but also social and family aspects in mental health interventions. It is suggested that the sample size be expanded in future studies to improve the robustness of the model. In summary, the study demonstrates the usefulness of artificial intelligence in the early detection of anxiety in young people and highlights the relevance of addressing multidimensional factors in the assessment and treatment of this condition.

Accuracy of progress monitoring decision rules to evaluate response to instruction with two computer adaptive tests.

Computer adaptive tests have become popular assessments to screen students for academic risk. Research is emerging regarding their use as progress monitoring tools to measure response to instruction. We evaluated the accuracy of the trend-line decision rule when applied to outcomes from a frequently used reading computer adaptive test (i.e., Star Reading [SR]) and frequently used math computer adaptive test (i.e., Star Math [SM]). Analyses of extant SR and SM data were conducted to inform conditions for simulations to determine the number of assessments required to yield sufficient sensitivity (i.e., probability of recommending an instructional change when a change was warranted) and specificity (i.e., probability of recommending maintaining an intervention when a change was not warranted) when comparing performance to goal lines based upon a future target score (i.e., benchmark) as well as normative comparisons (50th and 75th percentiles). The extant dataset of SR outcomes consisted of monthly progress monitoring data from 993 Grade 3, 804 Grade 4, and 709 Grade 5 students from multiple states in the United States northwest. Data for SM were also drawn from the northwest and contained outcomes from 518 Grade 3, 474 Grade 4, and 391 Grade 5 students. Grade level samples were predominately White (range = 59.89%-67.72%) followed by Latinx (range = 9.65%-15.94%). Results of simulations suggest that when data were collected once a month, seven, eight, and nine observations were required to support low-stakes decisions with SR for Grades 3, 4, and 5, respectively. For SM, nine, ten, and eight observations were required for Grades, 3, 4, and 5, respectively. Given the length of time required to support reasonably accurate decisions, recommendations to consider other types of assessments and decision-making frameworks for academic progress monitoring are provided.

Basing intubation of acutely hypoxemic patients on physiologic principles.

The decision to intubate a patient with acute hypoxemic respiratory failure who is not in apparent respiratory distress is one of the most difficult clinical decisions faced by intensivists. A conservative approach exposes patients to the dangers of hypoxemia, while a liberal approach exposes them to the dangers of inserting an endotracheal tube and invasive mechanical ventilation. To assist intensivists in this decision, investigators have used various thresholds of peripheral or arterial oxygen saturation, partial pressure of oxygen, partial pressure of oxygen-to-fraction of inspired oxygen ratio, and arterial oxygen content. In this review we will discuss how each of these oxygenation indices provides inaccurate information about the volume of oxygen transported in the arterial blood (convective oxygen delivery) or the pressure gradient driving oxygen from the capillaries to the cells (diffusive oxygen delivery). The decision to intubate hypoxemic patients is further complicated by our nescience of the critical point below which global and cerebral oxygen supply become delivery-dependent in the individual patient. Accordingly, intubation requires a nuanced understanding of oxygenation indexes. In this review, we will also discuss our approach to intubation based on clinical observations and physiologic principles. Specifically, we consider intubation when hypoxemic patients, who are neither in apparent respiratory distress nor in shock, become cognitively impaired suggesting emergent cerebral hypoxia. When deciding to intubate, we also consider additional factors including estimates of cardiac function, peripheral perfusion, arterial oxygen content and its determinants. It is not possible, however, to pick an oxygenation breakpoint below which the benefits of mechanical ventilation decidedly outweigh its hazards. It is futile to imagine that decision making about instituting mechanical ventilation in an individual patient can be condensed into an algorithm with absolute numbers at each nodal point. In sum, an algorithm cannot replace the presence of a physician well skilled in the art of clinical evaluation who has a deep understanding of pathophysiologic principles.

Cincinnati Prehospital Stroke Scale (CPSS) as a Screening Tool for Early Identification of Cerebral Large Vessel Occlusions; a Systematic Review and Meta-analysis.

Introduction: Large vessel occlusion (LVO) strokes are associated with worse functional outcomes and higher mortality rates. In the present systematic review and meta-analysis, we evaluated the diagnostic yield of the Cincinnati Prehospital Stroke Scale (CPSS) in detecting LVO. Methods: We performed an extensive systematic search among online databases including Medline, Embase, Web of Science, and Scopus, until July 31st, 2023. We also conducted a manual search on Google and Google scholar, along with citation tracking to supplement the systematic search in retrieving all studies that evaluated the diagnostic accuracy of the CPSS in detecting LVO among patients suspected to stroke. Results: Fourteen studies were included in the present meta-analysis. CPSS showed the sensitivity of 97% (95% CI: 87%-99%) and the specificity of 17% (95% CI: 4%-54%) at the cut-off point of ≥1. The optimal threshold was determined to be ≥2, with a sensitivity of 82% (95% CI: 74%-88%) and specificity of 62% (95% CI: 48%-74%) in detecting LVO. At the highest cut-off point of ≥3, the CPSS had the lowest sensitivity of 60% (95% CI: 51%-69%) and the highest specificity of 81% (95% CI: 71%-88%). Sensitivity analyses showed the robustness of the results regardless of study population, inclusion of hemorrhagic stroke patients, pre-hospital or in-hospital settings, and the definition of LVO. Conclusion: A very low level of evidence demonstrated that CPSS, with a threshold set at ≥2, is a useful tool for identifying LVO stroke and directing patients to CSCs, both in prehospital and in-hospital settings.

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis.

This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57-0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.

Development and Validation of a Prediction Model for 1-Year Mortality in Patients With a Hematologic Malignancy Admitted to the ICU.

OBJECTIVES: To develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU. DESIGN: A retrospective cohort study. SETTING: Five university hospitals in the Netherlands between 2002 and 2015. PATIENTS: A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventilation, lowest platelet count, acute kidney injury, maximum heart rate, and type of malignancy. A bootstrap procedure reduced overfitting and improved the model's generalizability. This involved estimating the optimism in the initial model and shrinking the regression coefficients accordingly in the final model. We assessed performance using internal-external cross-validation by center and compared it with the Acute Physiology and Chronic Health Evaluation II model. Additionally, we evaluated clinical usefulness through decision curve analysis. The overall 1-year mortality rate observed in the study was 62% (95% CI, 59-65). Our 13-variable prediction model demonstrated acceptable calibration and discrimination at internal-external validation across centers (C-statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model (C-statistic 0.61; 95% CI, 0.57-0.65). Decision curve analysis indicated overall net benefit within a clinically relevant threshold probability range of 60-100% predicted 1-year mortality. CONCLUSIONS: Our newly developed 13-variable prediction model predicts 1-year mortality in hematologic malignancy patients admitted to the ICU more accurately than the Acute Physiology and Chronic Health Evaluation II model. This model may aid in shared decision-making regarding the continuation of ICU care and end-of-life considerations.

Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma.

This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model's performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients.

Clinical prediction models in children that use repeated measurements with time-varying covariates: a scoping review.

BACKGROUND: Emerging evidence suggests that clinical prediction models that use repeated (time-varying) measurements within each patient may have higher predictive accuracy than models that use patient information from a single measurement. OBJECTIVE: To determine the breadth of the published literature reporting the development of clinical prediction models in children that use time-varying predictors. DATA SOURCES: MEDLINE, EMBASE and Cochrane databases. ELIGIBILITY CRITERIA: We included studies reporting the development of a multivariable clinical prediction model in children, with or without validation, to predict a repeatedly measured binary or time-to-event outcome and utilizing at least one repeatedly measured predictor. SYNTHESIS METHODS: We categorized included studies by the method used to model time-varying predictors. RESULTS: Of 99 clinical prediction model studies that had a repeated measurements data structure, only 27 (27%) used methods that incorporated the repeated measurements as time-varying predictors in a single model. Among these 27 time-varying prediction model studies, we grouped model types into nine categories: time-dependent Cox regression, generalized estimating equations, random effects model, landmark model, joint model, neural network, K-nearest neighbor, support vector machine and tree-based algorithms. Where there was comparison of time-varying models to single measurement models, using time-varying predictors improved predictive accuracy. CONCLUSIONS: Various methods have been used to develop time-varying prediction models in children, but there is a paucity of pediatric time-varying models in the literature. Incorporating time-varying covariates in pediatric prediction models may improve predictive accuracy. Future research in pediatric prediction model development should further investigate whether incorporation of time-varying covariates improves predictive accuracy.

Enhancing discharge decision-making through continuous monitoring in an acute admission ward: a randomized controlled trial.

In Acute Admission Wards, vital signs are commonly measured only intermittently. This may result in failure to detect early signs of patient deterioration and impede timely identification of patient stability, ultimately leading to prolonged stays and avoidable hospital admissions. Therefore, continuous vital sign monitoring may improve hospital efficacy. The objective of this randomized controlled trial was to evaluate the effect of continuous monitoring on the proportion of patients safely discharged home directly from an Acute Admission Ward. Patients were randomized to either the control group, which received usual care, or the sensor group, which additionally received continuous monitoring using a wearable sensor. The continuous measurements could be considered in discharge decision-making by physicians during the daily bedside rounds. Safe discharge was defined as no unplanned readmissions, emergency department revisits or deaths, within 30 days after discharge. Additionally, length of stay, the number of Intensive Care Unit admissions and Rapid Response Team calls were assessed. In total, 400 patients were randomized, of which 394 completed follow-up, with 196 assigned to the sensor group and 198 to the control group. The proportion of patients safely discharged home was 33.2% in the sensor group and 30.8% in the control group (p = 0.62). No significant differences were observed in secondary outcomes. The trial was terminated prematurely due to futility. In conclusion, continuous monitoring did not have an effect on the proportion of patients safely discharged from an Acute Admission Ward. Implementation challenges of continuous monitoring may have contributed to the lack of effect observed. Trial registration: https://clinicaltrials.gov/ct2/show/NCT05181111 . Registered: January 6, 2022.

A deep-learning approach to predict bleeding risk over time in patients on extended anticoagulation therapy.

BACKGROUND: Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for the clinical changes and events that occur after the baseline visit, which can modify risk of bleeding. However, it is difficult to develop predictive models from the routine follow-up clinical interviews, which are irregular sequences of multivariate time series data. OBJECTIVES: To demonstrate that deep learning can incorporate patient time series follow-up data to improve prediction of major bleeding. METHODS: We used the baseline and follow-up data that were collected over 8 years in a longitudinal cohort study of 2542 patients, of whom 118 had major bleeding. Four supervised neural network-based machine-learning models were trained on the baseline, follow-up, or both datasets using 70% of the data. The performance of these models was evaluated, along with modified versions of 6 previously developed clinical models, on the remaining 30% of the data. RESULTS: An ensemble of feedforward and recurrent neural networks that used the baseline and follow-up data was the best-performing model, achieving a sensitivity and a specificity of 61% and 82%, respectively, in identifying major bleeding, and it outperformed the previously developed clinical models in terms of area under the receiver operating characteristic curve (82%) and area under the precision-recall curve (14%). CONCLUSION: Time series follow-up data can improve major bleeding prediction in patients on extended anticoagulation therapy.

Pulmonary embolism risk stratification: external validation of the 4-level Clinical Pretest Probability Score (4PEPS).

Background: The 4-level clinical pretest probability score (4PEPS) was recently introduced as a clinical decision rule for the diagnosis of pulmonary embolism (PE). Based on the score, patients are classified into clinical pretest probability categories (c-PTP). The "very low" category aims at excluding PE without further testing; "low" and "moderate" categories require D-dimer testing with specific thresholds, while patients with a "high" pretest directly proceed to imaging. Objectives: To provide further external validation of the 4PEPS model. Methods: The 4PEPS was applied to a previously collected prospective database of 756 patients with clinically suspected PE enrolled from European emergency departments in 2002 to 2003. The safety threshold for the failure rate in our study was calculated at 1.95% based on a 26% prevalence of PE in our study, as per the International Society on Thrombosis and Haemostasis Scientific and Standardization Committee guidance. Results: Patients were classified as follows: 90 (12%) in the very low c-PTP group, of whom 5 (5.6%; 95% CI, 2.4%-12.4%) had PE; 363 (49%) in the low c-PTP group, of whom 34 had PE (9.4%); 246 (34%) in the moderate c-PTP group, of whom 124 (50%) had PE; and 35 (5%) in the high c-PTP group of whom 30 (86%) had PE. Overall, the failure rate of the 4PEPS was 9/734 (1.2%; 95% CI, 0.59%-2.23%) Overall, 9 out of 734 patients (1.2%; 95% CI, 0.59%-2.23%) were diagnosed with PE despite a negative 4PEPS rule; 5 (5.6%) from the very low c-PTP group, 3 (1.4%) in the low c-PTP group, and 1 (3.2%) in the moderate c-PTP group. Conclusion: We provide external validation data of the 4PEPS. In this high-prevalence cohort (26% prevalence), PE prevalence in the very low-risk group was higher than expected. A prospective validation study is needed before implementing the 4PEPS model in routine clinical practice.

Personality heterophily and friendship as drivers for successful cooperation.

Cooperation is widespread and arguably a pivotal evolutionary force in maintaining animal societies. Yet, proximately, what underlying motivators drive individuals to cooperate remains relatively unclear. Since 'free-riders' can exploit the benefits by cheating, selecting the right partner is paramount. Such decision rules need not be based on complex calculations and can be driven by cognitively less-demanding mechanisms, like social relationships (e.g. kinship, non-kin friendships, dyadic tolerance), social status (e.g. dominance hierarchies) and personalities (social and non-social traits); however, holistic evidence related to those mechanisms is scarce. Using the classical 'loose-string paradigm', we tested cooperative tendencies of a hierarchical primate, the long-tailed macaque (Macaca fascicularis). We studied three groups (n = 21) in their social settings, allowing partner choice. We supplemented cooperation with observational and experimental data on social relationships, dominance hierarchies and personality. Friendship and dissimilarities in non-social 'exploration' and 'activity-sociability' personality traits predicted the likelihood of cooperative dyad formation. Furthermore, the magnitude of cooperative success was positively associated with friendship, low rank-distance and dissimilarity in the activity-sociability trait. Kinship did not affect cooperation. While some findings align with prior studies, the evidence of (non-social) personality heterophily promoting cooperation may deepen our understanding of the proximate mechanisms and, broadly, the evolution of cooperation.