Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review.

OBJECTIVES: To assess improvement in the completeness of reporting coronavirus (COVID-19) prediction models after the peer review process. STUDY DESIGN AND SETTING: Studies included in a living systematic review of COVID-19 prediction models, with both preprint and peer-reviewed published versions available, were assessed. The primary outcome was the change in percentage adherence to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) reporting guidelines between pre-print and published manuscripts. RESULTS: Nineteen studies were identified including seven (37%) model development studies, two external validations of existing models (11%), and 10 (53%) papers reporting on both development and external validation of the same model. Median percentage adherence among preprint versions was 33% (min-max: 10 to 68%). The percentage adherence of TRIPOD components increased from preprint to publication in 11/19 studies (58%), with adherence unchanged in the remaining eight studies. The median change in adherence was just 3 percentage points (pp, min-max: 0-14 pp) across all studies. No association was observed between the change in percentage adherence and preprint score, journal impact factor, or time between journal submission and acceptance. CONCLUSIONS: The preprint reporting quality of COVID-19 prediction modeling studies is poor and did not improve much after peer review, suggesting peer review had a trivial effect on the completeness of reporting during the pandemic.

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.

New Strategies to Explain Organizational Resilience on the Firms: A Cross-Countries Configurations Approach

Organizations need to develop their resilience to foster future success to survive complex environments. This research conducts a comparative analysis to understand firms’ strategies in a “black swan” event. We use the “strategy tripod” to operationalize resilience theory and explain the configurations or pathways that lead to high organizational resilience in a crisis context. The data correspond to 1936 firms drawn from the “Enterprise Survey 2020 for Innovation and Entrepreneurship in China (ESIEC)”, and to 66 Central American firms drawn from the “World Bank 2020 Enterprise Surveys” are also analyzed. The methodological approach fuzzy set qualitative comparative analysis (fsQCA) is applied. We discuss and analyze the strategies of companies in this “new normal”;our results establish that in the case of emerging economies, organizational innovation seems to be a necessary condition for becoming an organizational resilience to a black swan crisis (finding from both cases). We also found that labor flexibility and emotional intelligence for the case of firms from China, and adequate control of the turbulence environment for the cases of Central America, were also necessary conditions for each region. We further argue that digitalization depends on access to government support for its success. China reinforces its strategies in an intensification of human resources flexibility. In addition, they are better prepared for the “black swan” crisis, allowing them to adapt quickly and generate business model innovation to mitigate the effects of the pandemic in this “new normal”. In contrast, Central America needs rapid organization for organizational resilience. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Reporting of coronavirus disease 2019 prognostic models: the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis statement.

Evaluation of the validity and applicability of published prognostic prediction models for coronavirus disease 2019 (COVID-19) is essential, because determining the patients' prognosis at an early stage may reduce mortality. This study was aimed to utilize the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) to report the completeness of COVID-19-related prognostic models and appraise its effectiveness in clinical practice. A systematic search of the Web of Science and PubMed was performed for studies published until August 11, 2020. All models were assessed on model development, external validation of existing models, incremental values, and development and validation of the same model. TRIPOD was used to assess the completeness of included models, and the completeness of each item was also reported. In total, 52 publications were included, including 67 models. Age, disease history, lymphoma count, history of hypertension and cardiovascular disease, C-reactive protein, lactate dehydrogenase, white blood cell count, and platelet count were the commonly used predictors. The predicted outcome was death, development of severe or critical state, survival time, and length-of-hospital stay. The reported discrimination performance of all models ranged from 0.361 to 0.994, while few models reported calibration. Overall, the reporting completeness based on TRIPOD was between 31% and 83% [median, 67% (interquartile range: 62%, 73%)]. Blinding of the outcome to be predicted or predictors were poorly reported. Additionally, there was little description on the handling of missing data. This assessment indicated a poorly-reported COVID-19 prognostic model in existing literature. The risk of over-fitting may exist with these models. The reporting of calibration and external validation should be given more attention in future research.

SARS-CoV-2 pneumonia succesfully treated with cpap and cycles of tripod position: a case report.

BACKGROUND: Pneumonia induced by 2019 Coronavirus (COVID-19) is characterized by hypoxemic respiratory failure that may present with a broad spectrum of clinical phenotypes. At the beginning, patients may have normal lung compliance and be responsive to noninvasive ventilatory support, such as CPAP. However, the transition to more severe respiratory failure - Severe Acute Respiratory Syndrome (SARS-CoV-2), necessitating invasive ventilation is often abrupt and characterized by a severe V/Q mismatch that require cycles of prone positioning. The aim of this case is to report the effect on gas exchange, respiratory mechanics and hemodynamics of tripod (or orthopneic sitting position) used as an alternative to prone position in a patient with mild SARS-CoV-2 pneumonia ventilated with helmet CPAP. CASE PRESENTATION: A 77-year-old awake and collaborating male patient with mild SARS-CoV-2 pneumonia and ventilated with Helmet CPAP, showed sudden worsening of gas exchange without dyspnea. After an unsuccessful attempt of prone positioning, we alternated three-hours cycles of semi-recumbent and tripod position, still keeping him in CPAP. Arterial blood gases (PaO2/FiO2, PaO2, SaO2, PaCO2 and A/a gradient), respiratory (VE, VT, RR) and hemodynamic parameters (HR, MAP) were collected in the supine and tripod position. Cycles of tripod position were continued for 3 days. The patient had a clinically important improvement in arterial blood gases and respiratory parameters, with stable hemodynamic and was successfully weaned and discharged to ward 10 days after pneumonia onset. CONCLUSIONS: Tripod position during Helmet CPAP can be applied safely in patients with mild SARS-CoV-2 pneumonia, with improvement of oxygenation and V/Q matching, thus reducing the need for intubation.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.