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To develop clinical prediction rule (CPR) of physical activity 1 year after total hip arthroplasty (THA).Retrospective cohort study.University hospital with orthopedic surgery.The study group included 321 patients (56 men) who underwent primary THA (N=321).Not applicable.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.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%.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.

International audience; Background: Hemoptysis is not common in pulmonary embolism (PE) and lacks specificity for its diagnosis. However, this item is present in different validated scores that estimate the clinical probability of PE. The relevance of this item in clinical decision rules (CDRs) is not clearly established.Objective: The aim of this study was to evaluate the impact of removing the "hemoptysis" item from the PERC, YEARS, and PEGeD CDR in patients with low clinical probability of PE.Design: This was a post hoc analysis of two European prospective cohorts, which included 2968 patients presenting to the ED with a low clinical probability of PE (PROPER and PERCEPIC) and a 3-month follow-up. The primary endpoint was the false-negative rate of a CDR score without the hemoptysis item. Secondary endpoints included the potential reduction of chest imaging if the item hemoptysis was to be removed and risk stratification of the Geneva and Wells scores without the hemoptysis item.Results: Of 2968 patients included (mean ± SD age 46 ± 18 years, 53% female), 87 patients (3%) had a PE diagnosed at 3 months. A total of 2908 were followed-up at 3 months and analyzed. Using the PERC rule with and without the hemoptysis item, there were 13 and 14 missed cases of PE, respectively (failure rate 0.45% [95% CI 0.25%-0.78%] and 0.48% [95% CI 0.27%-0.82%]). Using the YEARS strategy, there were 11 missed PE cases with or without the hemoptysis item (false-negative rate 0.57% [95% CI 0.30%-1.05%]). With the PERC and YEARS rule, removing the hemoptysis item would have led to a 1% reduction in chest imaging. The PEGeD strategy was not modified by the removal of the hemoptysis item.Conclusions: The hemoptysis item could be safely removed from the PERC, YEARS, and PEGeD CDRs. However, there was no subsequent clinically relevant reduction of chest imaging.

Aims Iliosacral sarcoma resections have been shown to have high rates of local recurrence (LR) and poor overall survival. There is also no universal classification for the resection of pelvic sarcomas invading the sacrum. This study proposes a novel classification system and analyzes the survival and risk of recurrence, when using this system. Methods This is a retrospective analysis of 151 patients (with median follow-up in survivors of 44 months (interquartile range 12 to 77)) who underwent hemipelvectomy with iliosacral resection at a single centre between 2007 and 2019. The proposed classification differentiates the extent of iliosacral resection and defines types S1 to S6 (S1 resection medial and parallel to the sacroiliac joint, S2 resection through the ipsilateral sacral lateral mass to the neuroforamina, S3 resection through the ipsilateral neuroforamina, S4 resection through ipsilateral the spinal canal, and S5 and S6 contralateral sacral resections). Descriptive statistics and the chi-squared test were used for categorical variables, and the Kaplan-Meier survival analysis were performed. Results Resections were S1 in 25/151 patients (17%), S2 in 70/151 (46%), S3 in 33/151 (22%), S4 in 77/151 (11%), S5 in 4/151 (3%), and S6 in 2/151 (1%). An internal hemipelvectomy was performed in 113/151 patients (75%), and 38/151 patients (25%) had an external hemipelvectomy. The predominant types of sarcoma were high-grade osteosarcoma in 48/151 patients (32%), chondrosarcoma in 41/151 (27%), Ewing sarcoma in 33/151 (22%), pleomorphic sarcoma in 17/151 (11%), and others in 2/151 (8%). LR was found in 24/151 patients (15%) with S3, S5, with S6 resections showing the highest rate of LR (p = 0.038). Overall, 19/151 patients (16%) had evidence of metastastic disease at the time of surgery and these patients showed poorer survival when compared to patients with no metastasis. Conclusion The proposed classification can help to report and compare different surgical and reconstructive approaches in these difficult cases who are still have a considerable risk of LR. Cite this article: Bone Joint J 2022;104-B(2):290–296.

Study objective We aimed to train and validate the time to on-scene return of spontaneous circulation prediction models using time-to-event analysis among out-of-hospital cardiac arrest patients. Methods Using a Korean population-based out-of-hospital cardiac arrest registry, we selected a total of 105,215 adults with presumed cardiac etiologies between 2013 and 2018. Patients from 2013 to 2017 and from 2018 were analyzed for training and test, respectively. We developed 4 time-to-event analyzing models (Cox proportional hazard [Cox], random survival forest, extreme gradient boosting survival, and DeepHit) and 4 classification models (logistic regression, random forest, extreme gradient boosting, and feedforward neural network). Patient characteristics and Utstein elements collected at the scene were used as predictors. Discrimination and calibration were evaluated by Harrell's C-index and integrated Brier score. Results Among the 105,215 patients (mean age 70 years and 64% men), 86,314 and 18,901 patients belonged to the training and test sets, respectively. On-scene return of spontaneous circulation was achieved in 5,240 (6.1%) patients in the former set and 1,709 (9.0%) patients in the latter. The proportion of emergency medical services (EMS) management was higher and scene time interval longer in the latter. Median time from EMS scene arrival to on-scene return of spontaneous circulation was 8 minutes for both datasets. Classification models showed similar discrimination and poor calibration power compared to survival models; Cox showed high discrimination with the best calibration (C-index [95% confidence interval]: 0.873 [0.865 to 0.882]; integrated Brier score at 30 minutes: 0.060). Conclusion Incorporating time-to-event analysis could lead to improved performance in prediction models and contribute to personalized field EMS resuscitation decisions.

The wide spread of coronavirus disease 2019 is currently the most rigorous health threat, and the clinical outcomes of severe patients are extremely poor. In this study, we establish an early warning nomogram model related to severe versus common COVID-19. A total of 1059 COVID-19 patients were analyzed in the primary cohort and divided into common and severe according to the guidelines on the Diagnosis and Treatment of COVID-19 by the National Health Commission of China (7th version). The clinical data were collected for logistic regression analysis to assess the risk factors for severe versus common type. Furthermore, 123 COVID-19 patients were reviewed as the validation cohort to assess the performance of this model. Multivariate logistic analysis revealed that age, dyspnea, lymphocyte count, C-reactive protein and interleukin-6 were independent factors for prewarning the severe type occurrence. Then, the early warning nomogram model including these risk factors for inferring the severe disease occurrence out of common type of COVID-19 was constructed. The C-index of this nomogram in the primary cohort was 0.863, 95% confidence interval (CI) (0.836-0.889). Meanwhile, in the validation cohort, the C-index of this nomogram was 0.889, 95% CI (0.828-0.950). In both the primary cohort and validation cohorts, the calibration curve showed good agreement between prediction and actual probability. The early warning model shows that data at the very beginning including age, dyspnea, lymphocyte count, CRP, and IL-6 may prewarn the severe disease occurrence to some extent, which could help clinicians early and timely treatment.

To assess the utility of machine learning (ML) algorithms in predicting clinically relevant atrial high-rate episodes (AHREs), which can be recorded by a pacemaker. We aimed to develop ML-based models to predict clinically relevant AHREs based on the clinical parameters of patients with implanted pacemakers in comparison to logistic regression (LR). We included 721 patients without known atrial fibrillation or atrial flutter from a prospective multicenter (11 tertiary hospitals) registry comprising all geographical regions of Korea from September 2017 to July 2020. Predictive models of clinically relevant AHREs were developed using the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm. Model prediction training was conducted by seven hospitals, and model performance was evaluated using data from four hospitals. During a median follow-up of 18 months, clinically relevant AHREs were noted in 104 patients (14.4%). The three ML-based models improved the discrimination of the AHREs (area under the receiver operating characteristic curve: RF: 0.742, SVM: 0.675, and XGB: 0.745 vs. LR: 0.669). The XGB model had a greater resolution in the Brier score (RF: 0.008, SVM: 0.008, and XGB: 0.021 vs. LR: 0.013) than the other models. The use of the ML-based models in patient classification was associated with improved prediction of clinically relevant AHREs after pacemaker implantation.

Summary Background & aims Malnutrition is persistent in 50%–75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucial for nutritional intervention. The aim of this study was to develop and validate machine learning (ML) models to predict the malnutrition status of children with CHD. We used explainable ML methods to provide insight into the model's predictions and outcomes. Methods This prospective cohort study included consecutive children with CHD admitted to the hospital from December 2017 to May 2020. The cohort data were divided into the training and test data sets based on the follow-up time. The outcome of the study was CHD child malnutrition 1 year after surgery, the primary outcome was an underweight status, and the secondary outcomes were stunted and wasting status. We used five ML algorithms with multiple features to construct prediction models, and the performance of these ML models was measured by an area under the receiver operating characteristic curve (AUC) analysis. We also used the permutation importance and SHapley Additive exPlanations (SHAP) to determine the importance of the selected features and interpret the ML models. Results We enrolled 536 children with CHD who underwent complete repair. The proportions of children with an underweight, stunted, or wasting status 1 year after surgery were 18.1% (97/536), 12.1% (65/536), and 17.5% (94/536), respectively. All patients contributed to the generation of 115 useable features, which allowed us to build models to predict malnutrition. Five prediction algorithms were used, and the XGBoost model achieved the greatest AUC in all outcomes. The results obtained from the permutation importance and SHAP analyses showed that the 1-month postoperative WAZ-score, discharge WAZ score and preoperative WAZ score were the top 3 important features in predicting an underweight status in the XGBoost algorithm. Regarding the stunted status, the top 3 important features were the 1-month postoperative HAZ score, discharge HAZ score, and aortic clamping time. Regarding the wasting status, the top 3 important features were the hospital length of stay, formula intake, and discharge WHZ-score. We also used a narrative case report as an example to describe the clinical manifestations and predicted the primary outcomes of two children. Conclusions We developed an ML model (XGBoost) that provides accurate early predictions of malnutrition 1-year postoperatively in children with CHD. Because the ML model is explainable, it may better enable clinicians to better understand the reasoning underlying the outcome. Our study could aid in determining individual treatment and nutritional follow-up strategies for children with CHD.

There remains a lack of prognosis models for patients with chronic thromboembolic pulmonary hypertension (CTEPH). This study aims to develop a nomogram predicting 3-, 5-, and 7-year survival in patients with CTEPH and verify the prognostic model. Patients with CTEPH diagnosed in Fuwai Hospital were enrolled consecutively between May 2013 and May 2019. Among them, 70% were randomly split into a training set and the other 30% as a validation set for external validation. Cox proportional hazards model was used to identify the potential survival-related factors which were candidate variables for the establishment of nomogram and the final model was internally validated by the bootstrap method. A total of 350 patients were included in the final analysis and the median follow-up period of the whole cohort was 51.2 months. Multivariate analysis of Cox proportional hazards regression showed body mass index, mean right atrial pressure, N-terminal pro-brain natriuretic peptide (per 500 ng/ml increase in concentration), presence of anemia, and main treatment choice were the independent risk factors of mortality. The nomogram demonstrated good discrimination with the corrected C-index of 0.82 in the training set, and the C-index of 0.80 (95% CI: 0.70 to 0.91) in the external validation set. The calibration plots also showed a good agreement between predicted and actual survival in both training and validation sets. In conclusion, we developed an easy-to-use nomogram with good apparent performance using 5 readily available variables, which may help physicians to identify CTEPH patients at high risk for poor prognosis and implement medical interventions.

Background & aims: Patients who receive chemoradiotherapy or bioradiotherapy (CRT/BRT) for locally advanced head and neck squamous cell carcinoma (LAHNSCC) often experience high toxicity rates interfering with oral intake, causing tube feeding (TF) dependency. International guidelines recommend gastrostomy insertion when the expected use of TF exceeds 4 weeks. We aimed to develop and externally validate a prediction model to identify patients who need TF > 4 weeks and would benefit from prophylactic gastrostomy insertion. Methods: A retrospective multicenter cohort study was performed in four tertiary head and neck cancer centers in the Netherlands. The prediction model was developed using data from University Medical Center Utrecht and the Netherlands Cancer Institute and externally validated using data from Maastricht University Medical Center and Radboud University Medical Center. The primary endpoint was TF dependency >4 weeks initiated during CRT/BRT or within 30 days after CRT/BRT completion. Potential predictors were extracted from electronic health records and radiotherapy dose-volume parameters were calculated. Results: The developmental and validation cohort included 409 and 334 patients respectively. Multi variable analysis showed predictive value for pretreatment weight change, texture modified diet at baseline, ECOG performance status, tumor site, N classification, mean radiation dose to the contralateral parotid gland and oral cavity. The area under the receiver operating characteristics curve for this model was 0.73 and after external validation 0.62. Positive and negative predictive value for a risk of 90% or higher for TF dependency >4 weeks were 81.8% and 42.3% respectively. Conclusions: We developed and externally validated a prediction model to estimate TF-dependency >4 weeks in LAHNSCC patients treated with CRT/BRT. This model can be used to guide personalized decision-making on prophylactic gastrostomy insertion in clinical practice. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The objective of this study was to develop a diagnostic prediction model for diagnosis of malignant pleural effusion (MPE) from pleural fluid cytology (MPE score).Retrospective analysis of pleural fluid cytology was conducted in patients with MPE between 2018 and 2020. Multivariable logistic regression was used to explore the potential predictors. The selected logistic coefficients were transformed into a diagnostic predictive scoring system. Internal validation was done using the bootstrapping procedure.The data of pleural fluid cytology from 155 MPE patients were analyzed. Seventy-eight positive pleural cytology patients were found (50.32%). Lung cancer was the cancer most commonly sent for pleural fluid testing, with 66.67% positive cytology. The predictive indicators included pleural fluid protein4.64 g/dL, pleural fluid LDH555 IU/L, and pleural fluid sugar60 mg/dL. Lung mass from imaging and double tap for pleural cytology were used for the derivation of the diagnostic prediction model. The score-based model showed that the area under the receiver operating characteristic curve was 0.74 (95% CI 0.66-0.82). The developed MPE score ranged from zero to 17. The cut-off point was 15 with 88.31% of specificity, 37.18% of sensitivity, positive predictive value of 0.76, and negative predictive value of 0.58. The measurement of the calibration was illustrated using a calibration plot (p-value = 0.49 for the Hosmer-Lemeshow based goodness of fit). Internal validation with 1,000 bootstrap resampling showed a good discrimination.The MPE score, as the diagnostic prediction model can be used in planning for more efficient diagnosis of MPE in patients with cancer under MPE.

Rationale: Determining whether an individual has obstructive or central sleep apnea is fundamental to selecting the appropriate treatment. Objectives: Here we derive an automated breath-by-breath p...

Objective To identify the association between readily available laboratory biomarkers and the development of severe sepsis in children presenting to the emergency department (ED) with systemic inflammatory response syndrome (SIRS). Methods In this retrospective cohort study, ED patient encounters from June 2018 to June 2019 that triggered an automated sepsis alert based on SIRS criteria were analyzed. Encounters were included if the patient had any of the following laboratory tests sent within 6 h of ED arrival: C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), lactic acid, and procalcitonin. For each of the biomarkers, a receiver operating characteristic (ROC) curve was created for our primary outcome, severe sepsis within 24 h of ED disposition, and our secondary outcome, severe sepsis with a positive bacterial culture. For each ROC curve, we calculated the area under the curve (AUC) with 95% confidence intervals (95% CI) and created cutoff points to achieve 90% sensitivity and 90% sensitivity for the primary and secondary outcomes. Results During the study period, 4349/61,195 (7.1%) encounters triggered an automated sepsis alert. Of those, 1207/4349 (27.8%) had one of the candidate biomarkers sent within 6 h of ED arrival and were included in the study. A total of 100/1207 (8.3%) met criteria for severe sepsis within 24 h of arrival, and 41/100 severe sepsis cases (41%) were deemed culture-positive. Procalcitonin had the highest AUC for identifying severe sepsis [0.62 (95% CI 0.52–0.73)] while ESR and CRP had the highest AUC for culture-positive sepsis [0.68 (95% CI 0.47–0.89) and 0.67 (95% CI 0.53–0.81), respectively]. At 90% sensitivity for detecting severe sepsis, all of the biomarker threshold values fell within that laboratory test's normal range. At 90% specificity for severe sepsis, threshold values were as follows: procalcitonin 2.72 ng/mL, CRP 16.79 mg/dL, ESR 79.5 mm/h and lactic acid 3.6 mmol/L. Conclusion Our data indicate that CRP, ESR, lactic acid, and procalcitonin elevations were all specific, but not sensitive, in identifying children in the ED with SIRS who go on to develop severe sepsis.

To derive a clinical prediction rule of termination of resuscitation (TOR) for out-of-hospital cardiac arrest (OHCA) with pre-hospital defibrillation given.This was a retrospective multicenter cohort study performed in three emergency departments (EDs) of three regional hospitals from 1/1/2012 to 31/12/2018. Patients of OHCA aged ≥18 years old were included. Those with post-mortem changes, return of spontaneous circulation and receiving no resuscitation in EDs were excluded. A TOR rule was derived by logistic regression analysis based on demographics and end-tidal carbon dioxide level of included subjects with pre-hospital defibrillation given.There were 447 included patients had received pre-hospital defibrillation, in which 148 had return of spontaneous circulation (ROSC), with 22 survived to discharge (STD). Independent predictors for death on or before ED arrival (no ROSC) included EMS call to ED time 20 min and ETCO2 level 20 mmHg from first three sets. A 2-criteria rule predicting death on or before ED arrival by fulfilling both of the independent predictors had a specificity of 0.95 (95% CI 0.90-0.98) and positive predictive value (PPV) of 0.95 (95% CI 0.90-0.98), if 2nd set of ETCO2 was used. The positive likelihood ratio was 10.04 (95% CI 4.83-20.89).The 2-criteria TOR rule for OHCA patients with pre-hospital defibrillation had a high specificity and PPV for predicting death on or before ED arrival.

Studies have suggested that qSOFA can be used for early detection of sepsis immediately upon arrival at the emergency department (ED). Despite this, little is known about the risk factors associated with the subsequent diagnosis of sepsis among patients with qSOFA2 in the ED.This is a retrospective cohort study using ED data from a large tertiary medical center in Japan, 2018-2020. We included adult patients (aged ≥18 years) presenting to the ED with suspected infection (e.g., having a fever) and qSOFA2. We identified patients who developed sepsis based on the Sepsis-3 criteria, and compared patient characteristics (e.g., demographics, vital signs upon the initial triage, chief complaint, and comorbidities) between patients who developed sepsis or not. Additionally, we identified the potential risk factors of sepsis among patients with qSOFA2 using a multivariable logistic regression model.We identified 151 (7%) patients who developed sepsis among 2025 adult patients with suspected infection and qSOFA2. Compared with patients who did not develop sepsis, patients who developed sepsis were likely to be older and have vital signs suggestive of imminent sepsis (e.g., high respiratory rate). In the multivariable logistic regression model, the potential risk factors of sepsis among patients with qSOFA2 were older age (adjusted OR, 1.92 [95%CI 1.19-3.19]), vital signs suggestive of imminent sepsis (e.g., adjusted OR of altered mental status, 3.50 [95%CI 2.25-5.50]), receipt of oxygen therapy upon arrival at the ED (adjusted OR, 1.91 [95%CI 1.38-2.26]), chief complaint of sore throat (adjusted OR, 2.15 [95%CI 1.08-4.13]), and the presence of comorbid diabetes mellitus, ischemic heart disease, and chronic kidney disease (e.g., adjusted OR of diabetes mellitus, 1.47 [95%CI 1.10-1.96]). On the contrary, chief complaint of abdominal and chest pain were associated with a lower risk of sepsis (e.g., adjusted OR of abdominal pain, 0.26 [95%CI 0.14-0.45]).We found that older age, vital signs prognosticating sepsis, and the presence of some comorbidities were the potential risk factors of sepsis in patients with qSOFA2. These potential risk factors could be useful to efficiently recognize patients who might develop sepsis in the ED.

Whether the electrocardiography (ECG) serial changes predict outcomes in cardiac arrest survivors undergoing therapeutic hypothermia remains unclear.This retrospective observational study enrolled 366 adult nontraumatic cardiac arrest survivors who underwent therapeutic hypothermia in a tertiary transfer center during 2006-2018. The ECG at return of spontaneous circulation (ROSC), during hypothermia and after rewarming were analyzed. 295 cardiac arrest survivors were included. Compared with the survivors, the non-survivors had longer QRS durations at the ROSC (118.33 ± 32.47 ms vs 106.88 ± 29.78 ms, p 0.001) and after rewarming (99.26 ± 25.07 ms vs 93.03 ± 19.09 ms, p = 0.008). The enrolled patients were classified into 4 groups based on QRS duration at the ROSC and after rewarming, namely (1) narrow-narrow (narrow QRS at ROSC and narrow QRS after rewarming, n = 156), (2) narrow-wide (n = 29), (3) wide-narrow (n = 87), and (4) wide-wide (n = 23) group. The wide-wide group had the worst survival rates [odds ratio (OR) = 0.141, p = 0.001], followed by the narrow-wide group (OR 0.223, p = 0.003) and the wide-narrow group (OR 0.389, p = 0.003).In cardiac arrest survivors given therapeutic hypothermia, QRS durations at the ROSC, after rewarming and their changes may predict survival to hospital discharge.

Summary Background & aims A limited number of studies have developed simple anthropometric equations that can be implemented for predicting muscle mass in the local community. Several studies have suggested calf circumference as a simple and accurate surrogate maker for muscle mass. We aimed to develop and cross-validate a simple anthropometric equation, which incorporates calf circumference, to predict appendicular skeletal muscle mass (ASM) using dual-energy X-ray absorptiometry (DXA). Furthermore, we conducted a comparative validity assessment of our equation with bioelectrical impedance analysis (BIA) and two previously reported equations using similar variables. Methods ASM measurements were recorded for 1262 participants (837 men, 425 women) aged 40 years or older. Participants were randomly divided into the development or validation group. Stepwise multiple linear regression was applied to develop the DXA-measured ASM prediction equation. Parameters including age, sex, height, weight, waist circumference, and calf circumference were incorporated as predictor variables. Total error was calculated as the square root of the sum of the square of the difference between DXA-measured and predicted ASMs divided by the total number of individuals. Results The most optimal ASM prediction equation developed was: ASM (kg) = 2.955 × sex (men = 1, women = 0) + 0.255 × weight (kg) − 0.130 × waist circumference (cm) + 0.308 × calf circumference (cm) + 0.081 × height (cm) − 11.897 (adjusted R2 = 0.94, standard error of the estimate = 1.2 kg). Our equation had smaller total error and higher intraclass correlation coefficient (ICC) values than those for BIA and two previously reported equations, for both men and women (men, total error = 1.2 kg, ICC = 0.91; women, total error = 1.1 kg, ICC = 0.80). The correlation between DXA-measured ASM and predicted ASM by the present equation was not significantly different from the correlation between DXA-measured ASM and BIA-measured ASM. Conclusions The equation developed in this study can predict ASM more accurately as compared to equations where calf circumference is used as the sole variable and previously reported equations; it holds potential as a reliable and an effective substitute for estimating ASM.

Purpose We investigated clinical risk factors that predict poor 30-day headache outcomes among patients evaluated in the emergency department (ED) for post-traumatic headache (PTH). Methods This was an analysis of data from a randomized, placebo-controlled study of IV metoclopramide + diphenhydramine for acute PTH. Patients were enrolled during an ED visit and received telephone follow-up with a structured questionnaire 30 days later. The primary outcome was frequency of headaches 30 days after ED discharge. We used multivariable logistic regression models to determine which clinical variables were associated with frequent headaches at 30 days. Results In total, 160 patients were enrolled in the study. 134 (84%) patients completed the 30-day questionnaire and were included in the analysis, including 90 females and 44 males. 30 patients (22%, 95% CI = 0.16 to 0.30) reported frequent headaches at 30-day follow-up. In the multivariable analysis, female sex (OR = 4.03, 95% CI = 1.23±13.13), patients who blamed themselves for their injury (OR = 0.13, 95% CI = 0.04±0.45), and patients who were unsure if they sustained loss of consciousness during the traumatic incident (OR = 5.63, 95% CI = 1.89±16.78) were found to be associated with poor 30-day outcomes. Medication received in the ED and age were not associated. Conclusions More than 1 out of five patients treated in the ED for acute PTH experienced frequent headaches 30 days later. Women and patients who were uncertain as to whether they had experienced loss of consciousness were at increased risk of frequent PTH. Blaming oneself for the head trauma was associated with less frequent PTH.

Rationale: Natural history of preserved ratio impaired spirometry (PRISm), often defined as FEV1/FVC ≥lower limit of normal and FEV1

Background The main objective of the treatment of acute carbon monoxide (CO) poisoning is to prevent delayed neurological sequelae (DNS). However, today there is still no objective screening tool to identify patients at high risk of developing DNS. The aim of this study was to identify clinical factors that could predict DNS after acute charcoal-burning CO poisoning. Methods This prospective observational study was conducted from September 1, 2019 to August 31, 2020 in a single academic medical center. Patients older than 18 years of age suffering from charcoal-burning CO poisoning were included in the study. After acute recovery, patients were followed up for six weeks to investigate for DNS development. The clinical predictors of DNS were determined using a multivariate logistic regression model. Results Of the 217 patients—113 males (52.1%), median age 37.0 (27.5–51.5) years—included, 49 (22.6%) developed DNS. The multivariate logistic regression analysis revealed the independent predictors of DNS as a lower initial Glasgow Coma Scale (GCS) score (adjusted odds ratio (AOR): 0.73, 95% confidence interval (CI): 0.62–0.87), a longer duration of CO exposure (AOR: 2.18, 95% CI: 1.65–2.88), and the presence of acute brain lesions with high signal intensity on diffusion-weighted imaging (AOR: 5.22, 95% CI: 1.50–18.08). The created multivariate regression model predicted DNS development with high accuracy (area under the curve: 0.93, 95% CI: 0.89–0.97). Conclusion A low initial GCS score, longer exposure to CO and abnormal findings on diffusion-weighted magnetic resonance imaging can assist in the early identification of patients at high risk of DNS development.

This study aimed to identify characteristics and movement-based tests that predict upper quadrant musculoskeletal injury (UQI) in military personnel over a 12-month follow-up.A prospective observational cohort study of military members (n = 494; 91.9% male) was conducted. Baseline predictors associated with UQI were gathered through surveys and movement-based tests. Survey data included demographic information, injury history, and biosocial factors. Movement-based tests include the following: Y Balance Tests (YBT), Functional Movement Screen, Selective Functional Movement Assessment lumbar multisegmental mobility, modified-modified Schober, side bridge, ankle mobility, modified Sorensen, and passive lumbar extension. Self-reported UQI was collected through monthly online surveys, and 87% completed the follow-up. Univariate associations were determined between potential predictors and UQI. A forward, stepwise logistic regression model was used to identify the best combination of predictors for UQI.Twenty-seven had UQI. Univariate associations existed with three demographic (smoking,1 previous UQI, baseline upper quadrant function ≤90%), three pain-related (Selective Functional Movement Assessment rotation, side bridge, hurdle step), and six movement-based variables (YBT upper quarter (UQ) superolateral worst score ≤57.75 cm, YBT-UQ composite worst score ≤81.1%, failed shoulder clearance, Sorenson72.14 s, in-line lunge total score15, and in-line lunge asymmetry1). Smoking, baseline upper quadrant function ≤90%, and YBT-UQ composite score ≤81.1% predicted UQI in the logistic regression while controlling for age and sex. Presenting two or more predictors resulted in good specificity (85.6%; odds ratio, 4.8; 95% confidence interval, 2.2-10.8), and at least one predictor resulted in 81.5% sensitivity (odds ratio, 3.2; 95% confidence interval, 1.2-8.7).A modifiable movement-based test (YBT-UQ), perceived upper limb function, and smoking predicted UQI. A specific (two or more) and sensitive (at least one predictor) model could identify persons at higher risk.

Recent reports linked acute COVID-19 infection in hospitalized patients to cardiac abnormalities. Studies have not evaluated presence of abnormal cardiac structure and function before scanning in setting of COVD-19 infection. We sought to examine cardiac abnormalities in consecutive group of patients with acute COVID-19 infection according to the presence or absence of cardiac disease based on review of health records and cardiovascular imaging studies. We looked at independent contribution of imaging findings to clinical outcomes. After excluding patients with previous left ventricular (LV) systolic dysfunction (global and/or segmental), 724 patients were included. Machine learning identified predictors of in-hospital mortality and in-hospital mortality + ECMO. In patients without previous cardiovascular disease, LV EF

Thoracolumbar Injury Classification System (TLICS) score and Thoracolumbar AO Spine Injury Score (TLAOSIS) are the scores preferred to classify and treat thoracolumbar fractures. Our study evaluates the reliability of both as guidelines for treatment.Single-center and retrospective case series of 458 patients. Clinical variables, radiology, and treatment were analyzed. We classified fractures according to the AO Spine Thoracolumbar System and retrospectively applied both scales in 2 groups (surgical and conservative). A concordance analysis and statistical measures comparing both were performed.The patients were divided as follows: 257 patients (56.1%) in the conservative group and 201 patients (43.9%) in the surgical group. The concordance analysis between both scales was 89.7% (95% confidence interval, 86.5%-92.3%), and the Cohen kappa coefficient was 0.68 (95% confidence interval, 59%-76%). TLAOSIS had a higher tendency to classify patients in the gray zone (10.3% vs. 2.8%, P0.001), whereas TLICS had a more conservative nature (85.2% vs. 78.4%, P = 0.01). In the surgical group, the matching decision ratio was 29.9% for TLICS and 42.8% for TLAOSIS, but differences were found in TLICS being more conservative (70.1% vs. 57.2%, P = 0.01). In the conservative group, the matching decision ratio was 98.1% for both scales, being the main difference in the gray zone for TLAOSIS.Both scales have a good concordance in general, with TLICS being more conservative overall. They had rather low coincidence when predicting surgery. Because TLAOSIS placed more patients in the gray zone, we think it might be slightly better for giving surgeons more license to decide a surgical approach on certain controversial types of fractures.

Rationale: Most lung cancers are diagnosed at an advanced stage. Presymptomatic identification of high-risk individuals can prompt earlier intervention and improve long-term outcomes. Objectives: T...

The diagnosis of histological transformation of follicular lymphoma can be challenging and ambiguous. We investigated the distribution of the Ki-67 labeling index of histological transformation of follicular lymphoma and determined its cutoff value to predict poor outcomes. The diagnostic criteria for histological transformation were a diffuse pattern of proliferation and a proportion of large lymphoma cells ≥20%. Of the 1121 patients with follicular lymphoma, 171 (15%) showed histological transformation to diffuse large B-cell lymphoma. Of these, 76 patients, whose biopsies were obtained from the sites with the highest maximum standardized uptake values, according to the positron emission tomography findings, were included. The Ki-67 index ranged from 16.8% to 98.4% (median, 60.6%). In patients with histological transformation, the most significant differences were found in progression-free survival (p = 0.087, 58% vs. 87% at 2 years) and overall survival (p = 0.024, 53% vs. 85% at 5 years) when a 70% cutoff was used. Additionally, overall survival was significantly shorter in patients with histological transformation with maximum standardized uptake values of ≥20 (p < 0.0001) and absence of a follicular lymphoma component (p = 0.004). A Ki-67 index of ≥70% was a significant adverse factor for overall survival in patients with histological transformation of follicular lymphoma and may predict poor outcomes.

ObjectivesTo develop and cross-validate a multivariable clinical prediction model to identify invasive bacterial infections (IBI) and to identify patient groups who might benefit from new biomarkers.DesignProspective observational study.Setting12 emergency departments (EDs) in 8 European countries.PatientsFebrile children aged 0–18 years.Main outcome measuresIBI, defined as bacteraemia, meningitis and bone/joint infection. We derived and cross-validated a model for IBI using variables from the Feverkidstool (clinical symptoms, C reactive protein), neurological signs, non-blanching rash and comorbidity. We assessed discrimination (area under the receiver operating curve) and diagnostic performance at different risk thresholds for IBI: sensitivity, specificity, negative and positive likelihood ratios (LRs).ResultsOf 16 268 patients, 135 (0.8%) had an IBI. The discriminative ability of the model was 0.84 (95% CI 0.81 to 0.88) and 0.78 (95% CI 0.74 to 0.82) in pooled cross-validations. The model performed well for the rule-out threshold of 0.1% (sensitivity 0.97 (95% CI 0.93 to 0.99), negative LR 0.1 (95% CI 0.0 to 0.2) and for the rule-in threshold of 2.0% (specificity 0.94 (95% CI 0.94 to 0.95), positive LR 8.4 (95% CI 6.9 to 10.0)). The intermediate thresholds of 0.1%–2.0% performed poorly (ranges: sensitivity 0.59–0.93, negative LR 0.14–0.57, specificity 0.52–0.88, positive LR 1.9–4.8) and comprised 9784 patients (60%).ConclusionsThe rule-out threshold of this model has potential to reduce antibiotic treatment while the rule-in threshold could be used to target treatment in febrile children at the ED. In more than half of patients at intermediate risk, sensitive biomarkers could improve identification of IBI and potentially reduce unnecessary antibiotic prescriptions.

This study aims to determine the ratio of delayed graft function in renal transplant recipients from living donors and the predictive value of hemodialysis time before transplant for delayed graft function.We conducted a study on 116 adult patients who were diagnosed with end-stage kidney disease and were treated with hemodialysis and transplanted kidneys from living donors for 2 years (from June 2018 to June 2020). Delayed graft function event was collected for each patient.The recipients had a median age of 36.5 years old, in which 55.2% of them were men, 4.3% of them had the diabetic mellitus, and the median hemodialysis duration was 6 months. The ratio of positive panel-reactive antibody was 33.6% and vascular reconstruction of the donor's kidney was 16.4%. The ratio of delayed graft function was 12.2% (14 of 116 patients). Delayed graft function significantly related to positive panel-reactive antibody, long duration of hemodialysis before transplant, and vascular reconstruction of donor's kidney with P.001. Duration of hemodialysis before kidney transplant had a predictive value for delayed graft function (area under the curve, 0.83; P.001).Delayed graft function was not rare in renal transplant recipients from living donors. Duration of hemodialysis before kidney transplant was a good predictor for delayed graft function.

OBJECTIVE To apply a precision medicine approach to determine the optimal treatment regime for participants in an exercise (E), dietary weight loss (D), and D + E trial for knee osteoarthritis that would maximize their expected outcomes. METHODS Using data from 343 participants of the Intensive Diet and Exercise for Arthritis (IDEA) trial, we applied 24 machine-learning models to develop individualized treatment rules on 7 outcomes: Short Form 36 physical component score, weight loss, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain/function/stiffness scores, compressive force, and interleukin-6 level. The optimal model was selected based on jackknife value function estimates that indicate improvement in the outcomes if future participants follow the estimated decision rule compared to the optimal single, fixed treatment model. RESULTS Multiple outcome random forest was the optimal model for the WOMAC outcomes. For the other outcomes, list-based models were optimal. For example, the estimated optimal decision rule for weight loss indicated assigning the D + E intervention to participants with baseline weight not exceeding 109.35 kg and waist circumference above 90.25 cm, and assigning D to all other participants except those with a history of a heart attack. If applied to future participants, the optimal rule for weight loss is estimated to increase average weight loss to 11.2 kg at 18 months, contrasted with 9.8 kg if all participants received D + E (P = 0.01). CONCLUSION The precision medicine models supported the overall findings from IDEA that the D + E intervention was optimal for most participants, but there was evidence that a subgroup of participants would likely benefit more from diet alone for 2 outcomes.

BackgroundClinical decision rules (CDRs) are commonly used to guide imaging decisions in cervical spine injury (CSI) assessment despite limited evidence for their use in paediatric populations. We set out to determine CSI incidence, imaging rates and the frequency of previously identified CSI risk factors, and thus assess the projected impact on imaging rates if CDRs were strictly applied as a rule in our population.MethodsA single-centre prospective observational study on all aged under 16 years presenting for assessment of possible CSI to a tertiary paediatric emergency department over a year, commencing September 2015. CDR variables from the National Emergency X-Radiography Utilization Study (NEXUS) rule, Canadian C-Spine rule (CCR) and proposed Paediatric Emergency Care Applied Research Network (PECARN) rule were collected prospectively and applied post hoc.Results1010 children were enrolled; 973 had not received prior imaging. Of these, 40.7% received cervical spine imaging; 32.4% X-rays, 13.4% CT scan and 3% MRI. All three CDRs identified the five children (0.5%) with CSI who had not received prior imaging. If CDRs were strictly applied as a rule for imaging, projected imaging rates in our setting would be as follows: NEXUS-44% (95% CI 41% to 47.4%), CCR-at least 48.4% (95% CI 45.3% to 51.7%) and PECARN-68% (95% CI 65.1% to 71.1%).ConclusionCSIs were rare (0.5% of our cohort), however, 40% of children received imaging. CDRs have been designed to guide imaging decisions; if strictly applied as a rule for imaging, the CDRs assessed in this study would increase imaging rates. Projected rates differ considerably depending on the CDR applied. These findings highlight the need for a validated paediatric-specific cervical spine imaging CDR.

Acute cholecystitis can be difficult to diagnose in the emergency department (ED); no single finding can rule in or rule out the disease. A prediction score for the diagnosis of acute cholecystitis for use at the bedside would be of great value to expedite the management of patients presenting with possible acute cholecystitis. The 2013 Tokyo Guidelines is a validated method for the diagnosis of acute cholecystitis but its prognostic capability is limited. The purpose of this study was to prospectively validate the Bedside Sonographic Acute Cholecystitis (SAC) Score utilizing a combination of only historical symptoms, physical exam signs, and point-of-care ultrasound (POCUS) findings for the prediction of the diagnosis of acute cholecystitis in ED patients.This was a prospective observational validation study of the Bedside SAC Score. The study was conducted at two tertiary referral academic centers in Boston, Massachusetts. From April 2016 to March 2019, adult patients (≥18 years old) with suspected acute cholecystitis were enrolled via convenience sampling and underwent a physical exam and a focused biliary POCUS in the ED. Three symptoms and signs (post-prandial symptoms, RUQ tenderness, and Murphy's sign) and two sonographic findings (gallbladder wall thickening and the presence of gallstones) were combined to calculate the Bedside Sonographic Acute Cholecystitis (SAC) Score. The final diagnosis of acute cholecystitis was determined from chart review or patient follow-up up to 30 days after the initial assessment. In patients who underwent operative intervention, surgical pathology was used to confirm the diagnosis of acute cholecystitis. Sensitivity, specificity, PPV and NPV of the Bedside SAC Score were calculated for various cut off points.153 patients were included in the analysis. Using a previously defined cutoff of ≥ 4, the Bedside SAC Score had a sensitivity of 88.9% (95% CI 73.9%-96.9%), and a specificity of 67.5% (95% CI 58.2%-75.9%). A Bedside SAC Score of2 had a sensitivity of 100% (95% CI 90.3%-100%) and specificity of 35% (95% CI 26.5%-44.4%). A Bedside SAC Score of ≥ 7 had a sensitivity of 44.4% (95% CI 27.9%-61.9%) and specificity of 95.7% (95% CI 90.3%-98.6%).A bedside prediction score for the diagnosis of acute cholecystitis would have great utility in the ED. The Bedside SAC Score would be most helpful as a rule out for patients with a low Bedside SAC Score2 (sensitivity of 100%) or as a rule in for patients with a high Bedside SAC Score ≥ 7 (specificity of 95.7%). Prospective validation with a larger study is required.

Background During outbreak of Coronavirus Disease 2019 (COVID-19), healthcare providers are facing critical clinical decisions based on the prognosis of patients. Decision support tools of risk stratification are needed to predict outcomes in patients with different clinical types of COVID-19. Methods This retrospective cohort study recruited 2425 patients with moderate or severe COVID-19. A logistic regression model was used to select and estimate the factors independently associated with outcomes. Simplified risk stratification score systems were constructed to predict outcomes in moderate and severe patients with COVID-19, and their performances were evaluated by discrimination and calibration. Results We constructed two risk stratification score systems, named as STPCAL (including significant factors in the prediction model: number of clinical symptoms, the maximum body temperature during hospitalization, platelet count, C-reactive protein, albumin and lactate dehydrogenase) and TRPNCLP (including maximum body temperature during hospitalization, history of respiratory diseases, platelet count, neutrophil-to-lymphocyte ratio, creatinine, lactate dehydrogenase, and prothrombin time), to predict hospitalization duration for moderate patients and disease progression for severe patients, respectively. According to STPCAL score, moderate patients were classified into three risk categories for a longer hospital duration: low (Score 0–1, median = 8 days, with less than 20.0% probabilities), intermediate (Score 2–6, median = 13 days, with 30.0–78.9% probabilities), high (Score 7–9, median = 19 days, with more than 86.5% probabilities). Severe patients were stratified into three risk categories for disease progression: low risk (Score 0–5, with less than 12.7% probabilities), intermediate risk (Score 6–11, with 18.6–69.1% probabilities), and high risk (Score 12–16, with more than 77.9% probabilities) by TRPNCLP score. The two risk scores performed well with good discrimination and calibration. Conclusions Two easy-to-use risk stratification score systems were built to predict the outcomes in COVID-19 patients with different clinical types. Identifying high risk patients with longer stay or poor prognosis could assist healthcare providers in triaging patients when allocating limited healthcare during COVID-19 outbreak.

Efficient Emergency Department (ED) throughput depends on several factors, including collaboration and consultation with surgical services. The acute care surgery service (ACS) collaborated with ED to implement a new process termed "FASTPASS" (FP), which might improve patient-care for those with acute appendicitis and gallbladder disease. The aim of this study was to evaluate the 1-year outcome of FP.FASTPASS is a joint collaboration between ACS and ED. ED physicians were provided with a simple check-list for diagnosing young males (50-year old) with acute appendicitis (AA) and young males or females (50-year old) with gallbladder disease (GBD). Once ED deemed patients fit our FP check-list, patients were directly admitted (FASTPASSed) to the observation unit. The ACS then came to evaluate the patients for possible surgical intervention. We performed outcome analysis before and after the institution of the FP. Outcomes of interest were ED length of stay (LOS), time from ED to the operating room (OR) (door-to-knife), hospital LOS (HLOS), and cost.During our 1-year study period, for those patients who underwent GBD/AA surgery, 56 (26%) GBD and 27 (26%) AA patients met FP criteria. Compared to the non-FP patients during FP period, FP halved ED LOS for GBD (7.4 ± 3.0 versus 3.5 ± 1.7 h, P 0.001) and AA (6.7 ± 3.3 versus. 1.8 ± 1.6 h, P 0.001). Similar outcome benefits were observed for door-to-knife time, HLOS, and costs.In this study, the FP process improved ED throughput in a single, highly-trained ER leading to an overall improved patient care process. A future study involving multiple EDs and different disease processes may help decrease ED overcrowding and improve healthcare system efficiency.

Introduction Necrotising otitis externa (NOE) is a severe infection of the temporal bone. The traditional severity based staging system does not fully prognosticate all patients with NOE. We hypothesise that a patient response staging system would more accurately capture the disease process and guide prognosis. Methods We carried out a retrospective notes review of patients diagnosed with NOE from January 2017 to December 2018 in a regional tertiary referral centre. Patient outcomes from our proposed patient response staging system were compared to a modified previously published severity based Gleeson staging system with patients requiring prolonged treatment classified as having a poor outcome. Results A total of 34 patients were treated for NOE. The majority were male (n=24) and had diabetes (n=25). Patients with the most severe Gleeson staging did not have the worst outcome. Daily delay in resolution of otorrhoea was associated with an increased need for more than six weeks of treatment. Rapid responders are patients who had resolution of otalgia, otorrhoea and C-reactive protein normalisation within 14 days, and all were cured following standard 6 weeks of treatment. Conclusions The Gleeson staging system was valuable in assessing the extent of disease and all early Gleeson staged patients had good outcomes. However, patients with higher severity staging on the Gleeson system did not necessarily require prolonged treatment. There is a role for a joint approach in staging patients based on both modified Gleeson and treatment response, which would subsequently guide prognosis, duration of treatment and early diagnosis of potential fungal NOE.

Objectives This study aimed to identify factors, including physical functions and activities that affect quality of life (QOL) at discharge among patients with osteoporotic vertebral fractures. Methods Patients with osteoporotic vertebral fractures were included in our prospective cohort study. Multiple regression analysis was performed to determine the predictors of QOL at discharge using two models: model 1, basic medical information and physical functions at admission, and model 2, basic medical information, physical function, and activity after 4 weeks of admission. Results Multiple regression analysis (standard partial regression coefficients) using model 1 identified L2 to L4 bone mineral density (-0.2), Visual Analog Scale for pain during activity at admission (-0.31), and Revised Hasegawa Dementia Scale (HDS-R) score at admission (0.64) as factors affecting QOL at discharge. Multiple regression analysis using model 2 identified HDS-R at admission (0.64), Pain Catastrophizing Scale score at 4 weeks (-0.34), and knee extension muscle strength at 4 weeks (0.28) as factors affecting QOL at discharge. Conclusions Our results suggest that if patients have high bone mineral density, intense pain, and low cognitive function at admission, then low QOL at discharge will be predicted; however, improvement of pain catastrophizing and knee extension muscle strength during first the 4 weeks of admission may be able to improve QOL at discharge. Because patients in this study were Japanese only, it is important to exercise caution when applying our results to other populations.

Background The impact of the stage of cancer on perioperative mortality remains obscure. The purpose of this study was to investigate whether cancer stage influences 30-d mortality for gastric, pancreatic, and colorectal cancers. Methods Data were collected from the National Cancer Database for patients undergoing resections for cancers of the stomach, pancreas, colon, or rectum between 2004 and 2015. The main analysis was conducted among patients with cancer stages 1-3. A sensitivity analysis also included cancer stage 4. Descriptive statistics were used to compare the patients’ baseline characteristics. Generalized linear mixed models were used to evaluate the relationship between stage and 30-d mortality, controlling for other disease-, patient- and hospital-level factors. Pseudo R2 statistics (%Δ pseudo R2) were used to quantify the relative explanatory capacity of the variables to the model for 30-d mortality. All analyses were performed using SAS 9.4. Results The cohort included 24,468, 28,078, 176,285, and 64,947 patients with stomach, pancreas, colon, and rectal cancers, respectively. After adjusting for other variables, 30-d mortality was different by stage for all cancer types examined. The factor most strongly associated with 30-d mortality was age (%Δ pseudo R2 range 14%-39%). The prognostic impact of cancer stage (Stages 1, 2, or 3) on 30-d mortality was comparable to that of the Charlson comorbidity index. Conclusions Cancer stage contributes to explaining differences observed in short-term mortality for gastrointestinal cancers. Short-term mortality models would benefit by including more granular cancer stage, beyond disseminated status alone.

Background Length of stay (LOS) is an important measure of quality; however, estimating LOS for rare populations such as gastroschisis is problematic. Our objective was to identify explanatory variables for LOS and build a model to estimate LOS in neonates with simple gastroschisis. Methods In 73 neonates with simple gastroschisis (47% female, 67% White), statistical correlations for 31 potential explanatory variables for LOS were evaluated using multivariate linear regression. Poisson regression was used to estimate LOS in predetermined subpopulations, and a life table model was developed to estimate LOS for simple gastroschisis. Results Female sex (−2.4 d), “time to silo placement” (0.9 d), total parenteral nutrition days (0.6 d), need for any nasogastric feedings (11.4 d) and at discharge (−7 d), “feeding tolerance” (0.4 d), days to first postoperative stool (−0.3 d), and human milk exposure (−3.4 d) associated with LOS in simple gastroschisis. Estimated LOS for preterm neonates was longer than term infants (5.4 versus 4.6 wk) but similar for estimates based on sex and race. Based on these associations, we estimate that >50% of neonates with simple gastroschisis will be discharged by hospital day 35. Conclusions We identified several associations that explained variations in LOS and developed a novel model to estimate LOS in simple gastroschisis, which may be applied to other rare populations.

Background Blood stream infection (BSI) and sepsis are serious clinical conditions and identification of the disease-causing pathogen is important for patient management. The RISE (Rapid Identification of SEpsis) study was carried out to collect a cohort allowing high-quality studies on different aspects of BSI and sepsis. The aim of this study was to identify patients at high risk for BSI who might benefit most from new, faster, etiological testing using neutrophil to lymphocyte count ratio (NLCR) and Shapiro score. Methods Adult patients (≥ 18 years) presenting at the emergency department (ED) with suspected BSI were prospectively included between 2014 and 2016 at Örebro University Hospital. Besides extra blood sampling, all study patients were treated according to ED routines. Electronic patient charts were retrospectively reviewed. A modified Shapiro score (MSS) and NLCR were extracted and compiled. Continuous score variables were analysed with area under receiver operator characteristics curves (AUC) to evaluate the ability of BSI prediction. Results The final cohort consisted of 484 patients where 84 (17%) had positive blood culture judged clinically significant. At optimal cut-offs, MSS (≥3 points) and NLCR (> 12) showed equal ability to predict BSI in the whole cohort (AUC 0.71/0.74; sensitivity 69%/67%; specificity 64%/68% respectively) and in a subgroup of 155 patients fulfilling Sepsis-3 criteria (AUC 0.71/0.66; sensitivity 81%/65%; specificity 46%/57% respectively). In BSI cases only predicted by NLCR> 12 the abundance of Gram-negative to Gram-positive pathogens (n = 13 to n = 4) differed significantly from those only predicted by MSS ≥3 p (n = 7 to n = 12 respectively) (p Conclusions MSS and NLCR predicted BSI in the RISE cohort with similar cut-offs as shown in previous studies. Combining the MSS and NLCR did not increase the predictive performance. Differences in BSI prediction between MSS and NLCR regarding etiology need further evaluation.

Background Application of advanced body composition measurement methods is not practical in developing countries context due to cost and unavailability of facilities. This study generated ethnic specific body fat percent prediction equation for Ethiopian adults using appropriate data. Methods A cross-sectional study was carried ifrom February to April 2015 among 704 randomly selected adult employees of Jimma University. Ethnic specific Ethiopian body fat percent (BF%) prediction equation was developed using a multivariable linear regression model with measured BF% as dependent variable and age, sex, and body mass index as predictor variables. Agreement between fat percent measured using air displacement plethysmography and body fat percent estimated using Caucasian prediction equations was determined using Bland Altman plot. Results Comparison of ADP measured and predicted BF% showed that Caucasian prediction equation underestimated body fat percent among Ethiopian adults by 6.78% (P Conclusion The Caucasian prediction equation significantly underestimates body fat percent among Ethiopian adults regardless of ethnicity. Ethiopian ethnic-specific prediction equation can be used as a very simple, cheap, and cost-effective alternative for estimating body fat percent among Ethiopian adults for health care provision in the prevention of obesity and related morbidities and for research purposes.

Background Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation. Methods A total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories. Discussion This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring. Trial registration: PB_2016-00500, SwissEthics. Registered on 6 April 2020.

Background To develop an effective model of predicting fatal outcomes in the severe coronavirus disease 2019 (COVID-19) patients. Methods Between February 20, 2020 and April 4, 2020, consecutive confirmed 2541 COVID-19 patients from three designated hospitals were enrolled in this study. All patients received chest computed tomography (CT) and serological examinations at admission. Laboratory tests included routine blood tests, liver function, renal function, coagulation profile, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and arterial blood gas. The SaO2 was measured using pulse oxygen saturation in room air at resting status. Independent high-risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients. Results There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR = 1.184, 95% CI 1.061–1.321), panting (breathing rate ≥ 30/min) (HR = 3.300, 95% CI 2.509–6.286), lymphocyte count 9/L (HR = 2.283, 95% CI 1.779–3.267), and interleukin-6 (IL-6) > 10 pg/ml (HR = 3.029, 95% CI 1.567–7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC = 0.900, 95% CI 0.841–0.960, sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC = 0.811, 95% CI 0.763–0.961, sensitivity 77.3%, specificity 73.5%); in validation cohort 2 (AUC = 0.862, 95% CI 0.698–0.924, sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of IL-6 receiving tocilizumab were better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (P = 0.105 for training cohort, P = 0.133 for validation cohort 1, and P = 0.210 for validation cohort 2). Conclusions This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6.

Introduction Patients with suspected appendicitis remain a diagnostic challenge. The aim of this study was to validate risk prediction models, and to investigate diagnostic accuracy of ultrasonography and computed tomography (CT) in adults undergoing appendicectomy. Methods A retrospective case review was performed of patients aged 16–45 years having an appendicectomy between January 2019 and January 2020 at a tertiary referral centre. Primary outcomes were the accuracy of a high risk appendicitis risk score and ultrasonography and CT imaging modalities compared with histological reports following appendicectomy. Results A total of 206 patients (52% female) were included in the study. Removal of a histologically normal appendix was equally likely in men and women (13.1% vs 11.2% respectively, relative risk: 1.17, 95% confidence interval: 0.56–2.44, p=0.674). A high risk appendicitis score correctly identified 84.0% (79/94) of cases in men and 85.9% (67/78) of cases in women. Ultrasonography was reported as equivocal in 85.7% (18/21) of low risk women and 59.0% (23/39) of high risk women. CT correctly detected or excluded appendicitis in 75.0% (6/8) of low risk women and 88.5% (23/26) of high risk women. Conclusions This study suggests that risk prediction models may be useful in both women and men to identify appendicitis. Ultrasonography gave high rates of equivocal results and should not be relied on for the diagnosis of appendicitis. CT is a highly accurate diagnostic tool and could be considered in those at low risk where clinical suspicion remains to reduce negative appendicectomy rates.

Objective Patients with peripheral arterial occlusive disease (PAOD) face an increased risk of both lower limb amputation and death. To date, it has been challenging to predict the long term outcomes for PAOD. The aim was to develop a risk score to predict worse five year amputation free survival (AFS). Methods In this retrospective analysis of claims data, symptomatic PAOD patients were split into training and validation sets. Variables in the model were patient age and sex, Elixhauser comorbidities, and the 190 most common secondary diagnoses. Penalised Cox regression (least absolute shrinkage and selection operator [LASSO]) with tenfold cross validation for variable selection was performed and patients were categorised into five risk groups using the ten most important variables. All analyses were stratified by intermittent claudication (IC) and chronic limb threatening ischaemia (CLTI). Results In total, 87 293 patients with PAOD (female 45.3%, mean age 71.4 ± 11.1 years) were included in the analysis. The most important variable predicting worse five year AFS was patient age >80 years. The GermanVasc score exhibited good predictive accuracy both for IC (c statistic = 0.70, 95% confidence interval [CI] 0.69–0.71) and CLTI (c statistic = 0.69, 95% CI 0.68–0.70) with adequate calibration due largely to alignment of observed and expected risk. Depending on the cumulative point score, the five year risk of amputation or death ranged from 9% (low risk) to 48% (high risk) for IC, and from 25% to 88% for CLTI. Conclusion The GermanVasc score predicts worse five year AFS stratified for inpatients suffering from IC and CLTI, with good predictive accuracy. By separating low from high risk patients, the GermanVasc score may support patient centred consent.

Background To establish and validate a nomogram and corresponding web-based calculator to predict the survival of patients with Parkinson's disease (PD). Methods In this cohort study, we retrospectively evaluated patients (n=497) with PD using a two-stage design, from March 2004 to November 2007 and from July 2005 to July 2015. Predictive variables included in the model were identified by univariate and multiple Cox proportional hazard analyses in the training set. Results Independent prognostic factors including age, PD duration, and Hoehn and Yahr stage were determined and included in the model. The model showed good discrimination power with the area under the curve (AUC) values generated to predict 4-, 6-, and 8-year survival in the training set being 0.716, 0.783, and 0.814, respectively. In the validation set, the AUCs of 4- and 6-year survival predictions were 0.85 and 0.924, respectively. Calibration plots and decision curve analysis showed good model performance both in the training and validation sets. For convenient application, we established a web-based calculator (https://tangyl.shinyapps.io/PDprognosis/). Conclusions We developed a satisfactory, simple-to-use nomogram and corresponding web-based calculator based on three relevant factors to predict prognosis and survival of patients with PD. This model can aid personalized treatment and clinical decision-making.