Prognostic value of initial serum sodium level in predicting disease severity in patients with COVID-19: A multicenter retrospective study.

INTRODUCTION: Early prediction of coronavirus disease (COVID-19) severity is crucial. Hyponatremia has been linked to poor outcomes in hospitalized COVID-19 patients, but its association with mild cases is unclear. This study aimed to investigate whether initial serum sodium level is a risk factor for COVID-19 severity in patients with mild-to-moderate disease. METHODS: A multicenter retrospective cohort study was conducted in 10 hospitals in Fukui City, Japan, from July 1, 2020, to October 31, 2021. The study included 1055 adult patients with asymptomatic, mild, or moderate COVID-19 confirmed by a positive RT-PCR test. The primary outcome was the need for oxygen therapy after hospitalization, and the secondary outcome was the composite of in-hospital death and critical care interventions. The association between initial serum sodium level (at the emergency department or on admission) and outcomes was examined, adjusting for age, sex, hypertension, and pneumonia presence. RESULTS: Of the 1267 patients diagnosed with COVID-19 during the study period, 1055 were eligible (median age: 45 years; 54 % male). Hyponatremia was observed in 5.2 % of patients upon admission. A lower initial serum sodium level was associated with an increased risk of the need for oxygen therapy after hospitalization (adjusted odds ratio [OR] per 1 mmol/L lower, 1.12 [95 % confidence interval {CI}, 1.05-1.19]) and the composite of critical care and in-hospital death (adjusted OR per 1 mmol/L lower, 1.09 [95 % CI, 0.99-1.20]). CONCLUSIONS: Among patients with mild COVID-19, lower initial serum sodium level was a risk factor for COVID-19 progression.

Machine-Learning Approaches for Predicting the Need of Oxygen Therapy in Early-Stage COVID-19 in Japan: Multicenter Retrospective Observational Study.

Background: Early prediction of oxygen therapy in patients with coronavirus disease 2019 (COVID-19) is vital for triage. Several machine-learning prognostic models for COVID-19 are currently available. However, external validation of these models has rarely been performed. Therefore, most reported predictive performance is optimistic and has a high risk of bias. This study aimed to develop and validate a model that predicts oxygen therapy needs in the early stages of COVID-19 using a sizable multicenter dataset. Methods: This multicenter retrospective study included consecutive COVID-19 hospitalized patients confirmed by a reverse transcription chain reaction in 11 medical institutions in Fukui, Japan. We developed and validated seven machine-learning models (e.g., penalized logistic regression model) using routinely collected data (e.g., demographics, simple blood test). The primary outcome was the need for oxygen therapy (≥1 L/min or SpO2 ≤ 94%) during hospitalization. C-statistics, calibration slope, and association measures (e.g., sensitivity) evaluated the performance of the model using the test set (randomly selected 20% of data for internal validation). Among these seven models, the machine-learning model that showed the best performance was re-evaluated using an external dataset. We compared the model performances using the A-DROP criteria (modified version of CURB-65) as a conventional method. Results: Of the 396 patients with COVID-19 for the model development, 102 patients (26%) required oxygen therapy during hospitalization. For internal validation, machine-learning models, except for the k-point nearest neighbor, had a higher discrimination ability than the A-DORP criteria (P < 0.01). The XGboost had the highest c-statistic in the internal validation (0.92 vs. 0.69 in A-DROP criteria; P < 0.001). For the external validation with 728 temporal independent datasets (106 patients [15%] required oxygen therapy), the XG boost model had a higher c-statistic (0.88 vs. 0.69 in A-DROP criteria; P < 0.001). Conclusions: Machine-learning models demonstrated a more significant performance in predicting the need for oxygen therapy in the early stages of COVID-19.

Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study.

BACKGROUND: There is still room for improvement in the modified LEMON (look, evaluate, Mallampati, obstruction, neck mobility) criteria for difficult airway prediction and no prediction tool for first-pass success in the emergency department (ED). OBJECTIVE: We applied modern machine learning approaches to predict difficult airways and first-pass success. METHODS: In a multicenter prospective study that enrolled consecutive patients who underwent tracheal intubation in 13 EDs, we developed 7 machine learning models (eg, random forest model) using routinely collected data (eg, demographics, initial airway assessment). The outcomes were difficult airway and first-pass success. Model performance was evaluated using c-statistics, calibration slopes, and association measures (eg, sensitivity) in the test set (randomly selected 20% of the data). Their performance was compared with the modified LEMON criteria for difficult airway success and a logistic regression model for first-pass success. RESULTS: Of 10,741 patients who underwent intubation, 543 patients (5.1%) had a difficult airway, and 7690 patients (71.6%) had first-pass success. In predicting a difficult airway, machine learning models-except for k-point nearest neighbor and multilayer perceptron-had higher discrimination ability than the modified LEMON criteria (all, P≤.001). For example, the ensemble method had the highest c-statistic (0.74 vs 0.62 with the modified LEMON criteria; P<.001). Machine learning models-except k-point nearest neighbor and random forest models-had higher discrimination ability for first-pass success. In particular, the ensemble model had the highest c-statistic (0.81 vs 0.76 with the reference regression; P<.001). CONCLUSIONS: Machine learning models demonstrated greater ability for predicting difficult airway and first-pass success in the ED.

Calibration-Free Cuffless Blood Pressure Estimation Based on a Population With a Diverse Range of Age and Blood Pressure.

This study presents a new blood pressure (BP) estimation algorithm utilizing machine learning (ML). A cuffless device that can measure BP without calibration would be precious for portability, continuous measurement, and comfortability, but unfortunately, it does not currently exist. Conventional BP measurement with a cuff is standard, but this method has various problems like inaccurate BP measurement, poor portability, and painful cuff pressure. To overcome these disadvantages, many researchers have developed cuffless BP estimation devices. However, these devices are not clinically applicable because they require advanced preparation before use, such as calibration, do not follow international standards (81060-1:2007), or have been designed using insufficient data sets. The present study was conducted to combat these issues. We recruited 127 participants and obtained 878 raw datasets. According to international standards, our diverse data set included participants from different age groups with a wide variety of blood pressures. We utilized ML to formulate a BP estimation method that did not require calibration. The present study also conformed to the method required by international standards while calculating the level of error in BP estimation. Two essential methods were applied in this study: (a) grouping the participants into five subsets based on the relationship between the pulse transit time and systolic BP by a support vector machine ensemble with bagging (b) applying the information from the wavelet transformation of the pulse wave and the electrocardiogram to the linear regression BP estimation model for each group. For systolic BP, the standard deviation of error for the proposed BP estimation results with cross-validation was 7.74 mmHg, which was an improvement from 17.05 mmHg, as estimated by the conventional pulse-transit-time-based methods. For diastolic BP, the standard deviation of error was 6.42 mmHg for the proposed BP estimation, which was an improvement from 14.05mmHg. The purpose of the present study was to demonstrate and evaluate the performance of the newly developed BP estimation ML method that meets the international standard for non-invasive sphygmomanometers in a population with a diverse range of age and BP.

Multiple intubation attempts in the emergency department and in-hospital mortality: A retrospective observational study.

OBJECTIVES: Multiple intubation attempts in the Emergency Department (ED) have been associated with adverse events, but no study examined the influence of multiple intubation attempts on survival during hospitalization. Our aim was to compare one or more intubation attempts in the ED with risk of morbidity and mortality during hospitalization. METHODS: We conducted a single center retrospective analysis of all patients undergoing emergency intubation in the ED and then admission to the hospital, during September 2010 to April 2016. The primary exposure was multiple intubation attempts. The primary outcome was mortality during hospitalization after intubation in the ED. RESULTS: Of 181 patients, 63 (35%) required two or more attempts. We found no significant difference in mortality (p = 0.11), discharge from the hospital (p = 0.45), length of stay in hospital (p = 0.34), intensive care unit (ICU) (p = 0.32), ED (p = 0.81) or intubation period (p = 0.64), between one or more intubation attempts. After adjustment for the number of intubation trials, age, sex, intubation methods, first intubator training level and diagnostic category, use of medications during intubation was the only independent prognostic variable for hospital death (adjusted OR 0.21, 95%CI 0.1-0.45, p < 0.01). Number of trials to achieve successful intubation was not associated with discharge disposition (OR 0.77 95%CI 0.24-2.46, p = 0.66). Age (OR 0.95, 95%CI 0.93-0.98, p < 0.01) and brain injury as a diagnostic category (OR 0.15 95%CI 0.04-0.56, p < 0.01) were independent prognostic variables. CONCLUSIONS: We found multiple intubation attempts were not associated with increased mortality and morbidity during hospitalization.

Conservative therapy for appendicitis in children.

Question A 10-year-old girl who was seen in my office last week with acute-onset abdominal pain and fever was referred to an emergency department, was diagnosed with appendicitis, and was treated conservatively with antibiotics, without surgery. Has the paradigm for treating appendicitis changed, and which is the preferred treatment of appendicitis in children: antibiotics or appendectomy?Answer For more than 100 years, surgical management was the principal treatment of acute appendicitis. Potential adverse events associated with appendectomy include bleeding, surgical site infection, and ileus, as well as stress for children and their parents. The option of treating appendicitis with antibiotics has been known for decades, which has led to consideration of antibiotics alone as a therapeutic alternative to surgery for uncomplicated appendicitis. While there is a reasonable body of evidence in support of this practice in adults, the accumulation of evidence of the safety and effectiveness of non-operative management in children is ongoing. Large studies are still needed, and those are being conducted at this time, with results expected in the next few years.

Pulled elbow in children.

Question Our practice is seeing children with relatively minor injuries to their elbows, with a history of "swinging" them when their hands are being held to cross the road. Nothing is usually found on a physical examination. I know that this is likely a "pulled elbow." Can we manage this in the clinic setting rather than sending the family to the emergency department? What would be the best course of action in the clinic setting?Answer Pulled elbow, also called nursemaid's elbow, is a radial head subluxation caused by axial traction or a sudden pull of the extended pronated arm, and it is a very common phenomenon. The practice of swinging children while holding their hands should be abandoned. In the case of pulled elbow, the child usually avoids moving the affected arm, holding it close to his or her body, without considerable pain, and no obvious swelling or deformity can be seen. While a fracture should be excluded, pulled elbow can usually be identified based on this presentation. The reduction procedure can easily be done in the office setting, with an 80% success rate and no complications. The hyperpronation maneuver (holding the elbow at 90° and then firmly pronating the wrist) to reduce pulled elbow has been found to be better than a supination-flexion maneuver (holding the elbow at 90° with one hand, supinating and flexing the elbow rapidly with the other) and should be exercised first. When 2 trials of reduction are unsuccessful, the child's arm should be splinted and the family should be sent for further evaluation.

The optimal number of personnel for good quality of chest compressions: A prospective randomized parallel manikin trial.

BACKGROUND: Long durational chest compression (CC) deteriorates cardiopulmonary resuscitation (CPR) quality. The appropriate number of CC personnel for minimizing rescuer's fatigue is mostly unknown. OBJECTIVE: We determined the optimal number of personnel needed for 30-min CPR in a rescue-team. METHODS: We conducted a randomized, manikin trial on healthcare providers. We divided them into Groups A to D according to the assigned different rest period to each group between the 2 min CCs. Groups A, B, C, and D performed CCs at 2, 4, 6, and 8 min rest period. All participants performed CCs for 30 min with a different rest period; participants allocated to Groups A, B, C, and D performed, eight, five, four, and three cycles, respectively. We compared a quality change of CCs among these groups to investigate how the assigned rest period affects the maintenance of CC quality during the 30-min CPR. RESULTS: This study involved 143 participants (male 58 [41%]; mean age, 24 years,) for the evaluation. As participants had less rest periods, the quality of their CCs such as sufficient depth ratio declined over 30-min CPR. A significant decrease in the sufficient CC depth ratio was observed in the second to the last cycle as compared to the first cycle. (median changes; A: -4%, B: -3%, C: 0%, and D: 0% p < 0.01). CONCLUSIONS: A 6 min rest period after 2 min CC is vital in order to sustain the quality of CC during a 30-min CPR cycle. At least four personnel may be needed to reduce rescuer's fatigue for a 30-min CPR cycle when the team consists of men and women.