Generation and validation of in-hospital mortality prediction score in COVID-19 patients: Alba-score.

BACKGROUND: COVID-19 has a wide range of symptoms reported, which may vary from very mild cases (even asymptomatic) to deadly infections. Identifying high mortality risk individuals infected with the SARS-CoV-2 virus through a prediction instrument that uses simple clinical and analytical parameters at admission can help clinicians to focus on treatment efforts in this group of patients. METHODS: Data was obtained retrospectively from the electronic medical record of all COVID-19 patients hospitalized in the Albacete University Hospital Complex until July 2020. Patients were split into two: a generating and a validating cohort. Clinical, demographical and laboratory variables were included. A multivariate logistic regression model was used to select variables associated with in-hospital mortality in the generating cohort. A numerical and subsequently a categorical score according to mortality were constructed (A: mortality from 0% to 5%; B: from 5% to 15%; C: from 15% to 30%; D: from 30% to 50%; E: greater than 50%). These scores were validated with the validation cohort. RESULTS: Variables independently related to mortality during hospitalization were age, diabetes mellitus, confusion, SaFiO2, heart rate and lactate dehydrogenase (LDH) at admission. The numerical score defined ranges from 0 to 13 points. Scores included are: age ≥71 years (3 points), diabetes mellitus (1 point), confusion (2 points), onco-hematologic disease (1 point), SaFiO2 ≤ 419 (3 points), heart rate ≥ 100 bpm (1 point) and LDH ≥ 390 IU/L (2 points). The area under the curve (AUC) for the numerical and categorical scores from the generating cohort were 0.8625 and 0.848, respectively. In the validating cohort, AUCs were 0.8505 for the numerical score and 0.8313 for the categorical score. CONCLUSIONS: Data analysis found a correlation between clinical admission parameters and in-hospital mortality for COVID-19 patients. This correlation is used to develop a model to assist physicians in the emergency department in the COVID-19 treatment decision-making process.

Risk factors and evolution of calcium and parathyroid hormone levels in hungry bone syndrome after parthyroidectomy for primary hyperparathyroidism. / Factores de riesgo y evolución del calcio y hormona paratiroidea en el síndrome de hueso hambriento tras paratiroidectomía por hiperparatiroidismo primario.

INTRODUCTION: Hungry bone syndrome (HBS) is a complication occurring after parathyroid surgery that can cause severe and prolonged hypocalcemia. The study objective was to know the risk factors for HBS after surgery for primary hyperparathyroidism and its relationship with serum calcium and parathyroid hormone levels. MATERIAL AND METHODS: A case-control, observational, analytical study was conducted in patients who had undergone surgery for primary hyperparathyroidism in the past 10 years (2007-2016). Changes over time in serum calcium and PTH levels and the general characteristics of patients were analyzed. RESULTS: The incidence rate of HBS in our series was 12.2%. HBS was found to be significantly associated to thyroid surgery during the surgical procedure itself (adjusted odds ratio [aOR]=17.241), to age older than 68 years (aOR=6.666), and to lesions greater than 1.7cm (aOR=7.165). A statistically significant relationship was seen between presence of HBS and corrected serum calcium levels higher than the mean the day after surgery and one week and 3 months later, and also with PTH levels higher than the mean before, during, and one day after surgery. CONCLUSION: In our series, independent risk factors for development of HBS included patient age, lesion size, and whether or not the procedure was accompanied by thyroid surgery, which requires closer monitoring of mineral metabolism during the perioperative period.