Predicting 90-day survival of patients with COVID-19: Survival of Severely Ill COVID (SOSIC) scores.

BACKGROUND: Predicting outcomes of critically ill intensive care unit (ICU) patients with coronavirus-19 disease (COVID-19) is a major challenge to avoid futile, and prolonged ICU stays. METHODS: The objective was to develop predictive survival models for patients with COVID-19 after 1-to-2 weeks in ICU. Based on the COVID-ICU cohort, which prospectively collected characteristics, management, and outcomes of critically ill patients with COVID-19. Machine learning was used to develop dynamic, clinically useful models able to predict 90-day mortality using ICU data collected on day (D) 1, D7 or D14. RESULTS: Survival of Severely Ill COVID (SOSIC)-1, SOSIC-7, and SOSIC-14 scores were constructed with 4244, 2877, and 1349 patients, respectively, randomly assigned to development or test datasets. The three models selected 15 ICU-entry variables recorded on D1, D7, or D14. Cardiovascular, renal, and pulmonary functions on prediction D7 or D14 were among the most heavily weighted inputs for both models. For the test dataset, SOSIC-7's area under the ROC curve was slightly higher (0.80 [0.74-0.86]) than those for SOSIC-1 (0.76 [0.71-0.81]) and SOSIC-14 (0.76 [0.68-0.83]). Similarly, SOSIC-1 and SOSIC-7 had excellent calibration curves, with similar Brier scores for the three models. CONCLUSION: The SOSIC scores showed that entering 15 to 27 baseline and dynamic clinical parameters into an automatable XGBoost algorithm can potentially accurately predict the likely 90-day mortality post-ICU admission (sosic.shinyapps.io/shiny). Although external SOSIC-score validation is still needed, it is an additional tool to strengthen decisions about life-sustaining treatments and informing family members of likely prognosis.

Development and validation of a machine-learning ALS survival model lacking vital capacity (VC-Free) for use in clinical trials during the COVID-19 pandemic.

Introduction: Vital capacity (VC) is routinely used for ALS clinical trial eligibility determinations, often to exclude patients unlikely to survive trial duration. However, spirometry has been limited by the COVID-19 pandemic. We developed a machine-learning survival model without the use of baseline VC and asked whether it could stratify clinical trial participants and a wider ALS clinic population. Methods. A gradient boosting machine survival model lacking baseline VC (VC-Free) was trained using the PRO-ACT ALS database and compared to a multivariable model that included VC (VCI) and a univariable baseline %VC model (UNI). Discrimination, calibration-in-the-large and calibration slope were quantified. Models were validated using 10-fold internal cross validation, the VITALITY-ALS clinical trial placebo arm and data from the Emory University tertiary care clinic. Simulations were performed using each model to estimate survival of patients predicted to have a > 50% one year survival probability. Results. The VC-Free model suffered a minor performance decline compared to the VCI model yet retained strong discrimination for stratifying ALS patients. Both models outperformed the UNI model. The proportion of excluded vs. included patients who died through one year was on average 27% vs. 6% (VCI), 31% vs. 7% (VC-Free), and 13% vs. 10% (UNI). Conclusions. The VC-Free model offers an alternative to the use of VC for eligibility determinations during the COVID-19 pandemic. The observation that the VC-Free model outperforms the use of VC in a broad ALS patient population suggests the use of prognostic strata in future, post-pandemic ALS clinical trial eligibility screening determinations.

Clinical characteristics and risk factors for mortality in COVID-19 inpatients in Birjand, Iran: a single-center retrospective study.

BACKGROUND: The coronavirus disease 2019(COVID-19) has affected mortality worldwide. The Cox proportional hazard (CPH) model is becoming more popular in time-to-event data analysis. This study aimed to evaluate the clinical characteristics in COVID-19 inpatients including (survivor and non-survivor); thus helping clinicians give the right treatment and assess prognosis and guide the treatment. METHODS: This single-center study was conducted at Hospital for COVID-19 patients in Birjand. Inpatients with confirmed COVID-19 were included. Patients were classified as the discharged or survivor group and the death or non-survivor group based on their outcome (improvement or death). Clinical, epidemiological characteristics, as well as laboratory parameters, were extracted from electronic medical records. Independent sample T test and the Chi-square test or Fisher's exact test were used to evaluate the association of interested variables. The CPH model was used for survival analysis in the COVID-19 death patients. Significant level was set as 0.05 in all analyses. RESULTS: The results showed that the mortality rate was about (17.4%). So that, 62(17%) patients had died due to COVID-19, and 298 (83.6%) patients had recovered and discharged. Clinical parameters and comorbidities such as oxygen saturation, lymphocyte and platelet counts, hemoglobin levels, C-reactive protein, and liver and kidney function, were statistically significant between both studied groups. The results of the CPH model showed that comorbidities, hypertension, lymphocyte counts, platelet count, and C-reactive protein level, may increase the risk of death due to the COVID-19 as risk factors in inpatients cases. CONCLUSIONS: Patients with, lower lymphocyte counts in hemogram, platelet count and serum albumin, and high C-reactive protein level, and also patients with comorbidities may have more risk for death. So, it should be given more attention to risk management in the progression of COVID-19 disease.