Is the Increase in Record of Skin Wounds in Hospitalized Patients in Internal Medicine Units a Side Effect of the COVID-19 Pandemic?

Wound care is an important public health challenge that is present in all areas of the healthcare system, whether in hospitals, long term care institutions or in the community. We aimed to quantify the number of skin wounds reported after and during the COVID-19 pandemic. This descriptive longitudinal retrospective study compared of wound records in patients hospitalized in the internal medicine service during the first year of the COVID-19 pandemic (from 1 March 2020, to 28 February 2021) and previous-year to the outbreak (from 1 January 2019, to 31 December 2019). A sample of 1979 episodes was collected corresponding to 932 inpatients, 434 from the pre-pandemic year and 498 from the first year of COVID-19 pandemic; 147 inpatients were diagnosed with SARS-CoV-2 infection (3.2%). The percentage of wound episodes in the first year of the COVID-19 pandemic was higher than the pre-pandemic year, 17.9% (1092/6090) versus 15% (887/5906), with a significant increase in the months with the highest incidence of COVID cases. This study shows an increase in the burden of wound care during the COVID-19 pandemic, and it could be attributable to the increase in the number of patients hospitalized for SARS-CoV-2 infection in internal medicine units.

Clinical Burden of Inpatient Wound Care in Internal Medicine Units During the First Wave of COVID-19 Outbreak.

OBJECTIVE: To explore the clinical burden and epidemiologic profile of hospitalized patients with wounds during the first wave of COVID-19. METHODS: A retrospective and observational study was conducted to analyze the inpatient episodes of wound care in the University Hospital of Salamanca (Spain) during the initial COVID-19 crisis from March 1, 2020, to June 1, 2020. Data were collected from nursing care reports and clinical discharge reports. Included patients were 18 years or older, had a hospital length of stay of 1 day or longer, and were hospitalized in an internal medicine unit. Surgical and traumatic wounds and pediatric patients were excluded. RESULTS: A total of 116 patients and 216 wounds were included. The overall wound prevalence was 7.6%, and incidence was 3.5% in the internal medicine units. Pressure injuries (PIs) were the most common wound type, and patients with COVID-19 had significantly higher PI risk (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.1-4.0; P = .042). Significant differences in PI staging were noted: 83.2% of wounds in patients with COVID-19 were stages I-II versus 67.8% in patients without COVID-19; the probability of stage III-IV PIs among patients without COVID-19 was doubled (OR, 2.3; 95% CI, 1.2-4.5; P = .009). The probability of acute wounds tripled in patients with COVID-19 (OR, 3.7; 95% CI, 2.1-6.6; P < .001). Patients with COVID-19 also had longer mean hospital stays and higher ICU admission rates. No case fatality rate differences were observed. CONCLUSIONS: In this context of clinical practice, protocolized assessment and implementation of preventive measures must be ensured among older adult populations, patients with associated comorbidities, and ICU patients.

Are there differences between COVID-19 and non-COVID-19 inpatient pressure injuries? Experiences in Internal Medicine Units.

BACKGROUND: Pressure Injuries (PIs) are major worldwide public health threats within the different health-care settings. OBJECTIVE: To describe and compare epidemiological and clinical features of PIs in COVID-19 patients and patients admitted for other causes in Internal Medicine Units during the first wave of COVID-19 pandemic. DESIGN: A descriptive longitudinal retrospective study. SETTING: This study was conducted in Internal Medicine Units in Salamanca University Hospital Complex, a tertiary hospital in the Salamanca province, Spain. PARTICIPANTS: All inpatients ≥18-year-old admitted from March 1, 2020 to June 1, 2020 for more than 24 hours in the Internal Medicine Units with one or more episodes of PIs. RESULTS: A total of 101 inpatients and 171 episodes were studied. The prevalence of PI episodes was 6% and the cumulative incidence was 2.9% during the first-wave of COVID-19. Risk of acute wounds was four times higher in the COVID-19 patient group (p<0.001). Most common locations were sacrum and heels. Among hospital acquired pressure injuries a significant association was observed between arterial hypertension and diabetes mellitus in patients with COVID-19 diagnosis. CONCLUSION: During the first wave of COVID-19, COVID-19 patients tend to present a higher number of acute wounds, mainly of hospital origin, compared to the profile of the non-COVID group. Diabetes mellitus and arterial hypertension were identified as main associated comorbidities in patients with COVID-19 diagnosis.

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.