The modified NUTRIC score (mNUTRIC) is associated with increased 28-day mortality in critically ill COVID-19 patients: Internal validation of a prediction model.

BACKGROUND: High prevalence of malnutrition was found in critically ill COVID-19 patients. The modified Nutrition Risk in the Critically ill (mNUTRIC) score is frequently used for nutritional risk assessment in intensive care unit (ICU) COVID-19 patients. The aim of this study was to investigate the role of mNUTRIC score to predict 28-day mortality in critically ill COVID-19 patients admitted to ICU. METHODS: A cohort of consecutive COVID-19 critically ill patients admitted to ICU was retrospectively evaluated and the nutritional risk was assessed with the use of mNUTRIC score. A multivariable Cox regression model to predict 28-day mortality was therefore developed including the mNUTRIC as a covariate. Internal validation was performed using the bootstrap resampling technique to reduce possible bias in the estimated risks. The performance of the prediction model was assessed via calibration and discrimination. RESULTS: A total of 98 critically ill COVID-19 patients with a median age of 66 years (56-73 IQR), 81 (82.7%) males were included in this study. A high nutritional risk (mNUTRIC ≥5 points) was observed in 41.8% of our critically ill COVID-19 patients while a low nutritional risk (mNUTRIC <5 points) was observed in 58.2%. Forty-five patients (45.9%) died within 28 days after ICU admission. In multivariable model after internal validation, mNUTRIC ≥5 (optimism adjusted HR 2.38, 95% CI 1.08-5.25, p = 0.02) and high-sensitivity C-reactive protein values (CRP) (optimism adjusted HR 1.02, 95% CI 1.01-1.07, p = 0.005) were independent predictors of 28-day mortality. CONCLUSIONS: A high prevalence of malnutrition as revealed by mNUTRIC was found in our critically ill COVID-19 patients once admitted in ICU. After adjustment for covariables, mNUTRIC ≥5 and CRP levels were independently associated with 28-day mortality in critically ill COVID-19 patients. The final model revealed good discrimination and calibration. Nutritional risk assessment is essential for the management of critically ill COVID-19 patients as well as for outcome prediction.

COVID-19 Outbreak in Italy: Report on the First 124 Consecutive Patients Treated at Home.

North Italy emerged as an epicenter of COVID-19 in the Western world. The majority of studies of patients with COVID-19 have focused on hospitalized patients, and data on early outpatient treatment are limited. This research retrospectively examines consecutive symptomatic adults who did not present to a hospital but who experience laboratory confirmed (nasopharyngeal swabs) or probable COVID-19 infection. From March 12 to April 12, 2020, 124 consecutive patients with laboratory-confirmed COVID-19 infection (84%) or with epidemiologically linked exposure to a person with confirmed infection (16%) were managed at home. The diagnosis of pneumonia was made with a portable ultrasound. COVID-19 treatment was based on low-dose hydroxychloroquine with or without darunavir/cobicistat or azithromycin and enoxaparine for bedridden patients. The patients were monitored by telemedicine. The primary endpoints were clinical improvement or hospitalization, and the secondary endpoints were mortality at day 30 and at day 60. Forty-seven (37.9%) patients had mild COVID-19 infection, 44 (35.5%) had moderate COVID-19 infection, and 33 (26.6%) had severe COVID-19 infection. Four patients (3.2%) were hospitalized and there were no deaths at day 30 and at day 60. Only mild side effects were reported. Early home treatment of COVID-19 patients resulted in a low hospitalization rate with no deaths, with the limitations of the small sample size and that it was conducted within a single geographic area. We believe that this model may be easily reproduced in both cities and rural areas around the world to treat COVID-19 infection.

Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model.

BACKGROUND: COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. MATERIALS AND METHODS: We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model's discriminatory ability was assessed with Harrell's C-statistic and the goodness-of-fit was evaluated with calibration plot. RESULTS: 242 patients were included [median age, 64 years (56-71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6-18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82). CONCLUSIONS: We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.

BACKGROUND: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling. OBJECTIVE: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia. METHODS: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori. RESULTS: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively. CONCLUSIONS: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.

Specificity and reliability of prognostic indexes in intensive care evaluation: the spontaneous cerebral haemorrhage case.

OBJECTIVE: To determine the reliability of a generic index such as Simplified Acute Physiology Score II (SAPSII), compared with a specific one Intra Cerebral Haemorrhage score (ICH score), as an intensive care unit (ICU) outcome predictor when evaluating a general facility that frequently treats a specific type of patients - those with spontaneous cerebral haemorrhage. METHODS: The study cohort consisted of a random sample of patients (81) admitted to Modena's Policlinico Teaching Hospital's ICU with spontaneous ICH over a 24-month period. Main outcome measure SAPSII, ICH score, overall mortality. RESULTS: The mean ICH score for the 32 surviving patients was 3.41 +/- 1.012 while for the 49 deceased patients was of 4.24 +/- 0.855 (P = 0.000). The mean SAPSII value for the 32 surviving patients was 49.09 +/- 16.58 while for the 49 deceased patients was 49.51 +/- 15.93. SAPSII, ICH scores were analysed for mortality, by receiver operating characteristic curves: the area under the curve was significant for ICH, not-significant for SAPSII. CONCLUSIONS: Regional quality controls use generic prognostic indexes (SAPSII) in relation to mortality and outcome to assess ICUs, which is appropriate when dealing with a general facility when there is not a predominant type of patient, but it may bias the evaluation if the population with specific pathologies (ICH), not included in the general index, is statistically considerable, leading to an incorrect criticality assessment, an inappropriate strategic plan and the subsequent inefficient resource allocation.