Predictors of mortality in hospitalised patients with COVID-19: a 1-year case-control study.

BACKGROUND: A paucity of predictive models assessing risk factors for COVID-19 mortality that extend beyond age and gender in Latino population is evident in the current academic literature. OBJECTIVES: To determine the associated factors with mortality, in addition to age and sex during the first year of the pandemic. DESIGN: A case-control study with retrospective revision of clinical and paraclinical variables by systematic revision of clinical records was conducted. Multiple imputations by chained equation were implemented to account for missing variables. Classification and regression trees (CART) were estimated to evaluate the interaction of associated factors on admission and their role in predicting mortality during hospitalisation. No intervention was performed. SETTING: High-complexity centre above 2640 m above sea level (masl) in Colombia. PARTICIPANTS: A population sample of 564 patients admitted to the hospital with confirmed COVID-19 by PCR. Deceased patients (n=282) and a control group (n=282), matched by age, sex and month of admission, were included. MAIN OUTCOME MEASURE: Mortality during hospitalisation. MAIN RESULTS: After the imputation of datasets, CART analysis estimated 11 clinical profiles based on respiratory distress, haemoglobin, lactate dehydrogenase, partial pressure of oxygen to inspired partial pressure of oxygen ratio, chronic kidney disease, ferritin, creatinine and leucocytes on admission. The accuracy model for prediction was 80.4% (95% CI 71.8% to 87.3%), with an area under the curve of 78.8% (95% CI 69.63% to 87.93%). CONCLUSIONS: This study discloses new interactions between clinical and paraclinical features beyond age and sex influencing mortality in COVID-19 patients. Furthermore, the predictive model could offer new clues for the personalised management of this condition in clinical settings.

Fibrosis Management Algorithm in Secondary Liposculpture.

BACKGROUND: Liposuction is considered one of the most common procedures in plastic surgery. However, major postoperative complications such as visceral injury, fluid overload, and necrotizing fasciitis still occur. Likewise, minor complications such as ecchymosis, seromas, infections, and contour irregularities that do not threaten the life of the patient do generate significant dissatisfaction. Current evidence regarding the management of fibrosis after previous liposuction remains limited. OBJECTIVES: The objective of this article is to standardize a management algorithm based on the extensive experience and successful results of the primary author (G.M.). METHODS: Patients who underwent secondary liposculpture between August 2022 and May 2023 were evaluated prospectively. Inclusion criteria comprised females between 18 and 60 years old, nonsmokers, with a BMI < 35 kg/m2 and a history of previous body contouring surgeries. Identification of the patient's skin condition and subcutaneous lesions in the adipose tissue were obtained in detail. Statistical analysis of preoperative and postoperative medical photographs was also performed with the Fiji Biological image analyzer. RESULTS: Photographic analysis of preoperative and postoperative photographs showed a statistically significant difference between the areas affected by fibrosis (P < .001). The most frequent clinical findings were depressions in 99% of the females (74), followed by soft nodules in 95% (70), hard nodules in 81% (61), adhesions in 47% (35), and finally cutaneous bursas in 4%. CONCLUSIONS: Our classification system and management algorithm for fibrosis and contour irregularities is a safe and reliable tool. Results were objectively verified, yielding statistically significant outcomes.