Influencia de la pluripatología y comorbilidad en el ingreso hospitalario en una cohorte de pacientes de una unidad de continuidad asistencial Primaria-Interna / Influence of multipathology and comorbidity on hospital admission in a cohort of patients from a Primary-Internal continuous care unit

Objetivo: estimar la frecuencia de pluripatología y comorbilidad en pacientes seguidos en una unidad de continuidad asistencial Primaria-Interna, así como conocer sus características clínicas y la relación entre pluripatología y comorbilidad con el ingreso hospitalario. Diseño: estudio observacional retrospectivo de casos y controles. Emplazamiento: Unidad de Continuidad Asistencial de Atención Primaria y Medicina Interna (UCAPI) del Complejo Hospitalario Universitario de Albacete. Participantes: se analizaron 1.591 pacientes atendidos en una unidad de continuidad asistencial Primaria-Interna (292 con algún ingreso hospitalario en el último año y 1.299 sin ingresos). Mediciones principales: edad, sexo, problemas de salud, índice de Charlson, consumo de medicamentos y utilización de servicios sanitarios los 3 años previos. Resultados: del total de la muestra, un 18,4% (IC 95%:16,4-20,3) eran casos con algún ingreso. Presentaba pluripatología un 23,3% (IC 95%: 21,1-25,4) y comorbilidad (índice de Charlson ≥ 2) un 32,6% (IC 95%: 30,2-34,9). Fueron variables asociadas de forma independiente a ingreso hospitalario la pluripatología (OR: 2,51; IC 95%: 1,64-3,83; p < 0,001), comorbilidad (índice de Charlson ≥ 2) (OR: 1,81; IC 95%:1,18-2,78; p = 0,006), tener más de tres problemas de salud (OR: 1,49; IC 95%: 1,07-2,07; p = 0,017), contar con mayor número de consultas de Atención Primaria (AP) (OR: 1,01; IC 95%: 1,00-1,02; p = 0,005), de hospital (1,03; IC 95%: 1,01-1,05), p < 0,001) y realizar más visitas a urgencias hospitalarias (OR: 1,12, IC 95%: 1,07-1,17). Conclusiones: casi una cuarta parte de los pacientes seguidos en una unidad de continuidad asistencial entre Medicina interna y de Familia presenta pluripatología y un tercio, comorbilidad. La presencia de pluripatología y comorbilidad son características relacionadas con el ingreso hospitalario, junto con la mayor utilización de servicios sanitarios.(AU)

Objective: to estimate the frequency of multiple pathologies and comorbidity in patients followed up in a Primary-Internal care continuity unit (PICCU), and to ascertain their clinical characteristics and degree of association between multiple pathologies and comorbidity with hospital admission. Design: retrospective case-control observational study. Site: Primary Care and Internal Medicine Continuity of Care Unit (UCAPI) of Albacete Teaching Hospital. Participants: a total of 1591 patients treated in a PICCU (292 with a hospital admission in the last year and 1299 without admission) were analyzed. Main measurements: age, sex, health problems, Charlson index, drug use and use of health services in the previous three years. Results: of the total sample, 18.4% (95% CI: 16.4-20.3) were cases with an admission; 23.3% had multiple pathologies (95% CI: 21.1-25.4) and comorbidity (Charlson index ≥ 2) 32.6% (95% CI: 30.2-34.9). Variables independently associated with hospital admission were multiple pathologies (OR: 2.51; 95% CI: 1.64-3.83; P <0.001), comorbidity (Charlson ≥ 2) (OR: 1.81; 95% CI): 1.18-2.78; P = 0.006), more than three health problems (OR: 1.49; 95% CI: 1.07-2.07; P = 0.017), a higher number of primary care consultations (OR: 1.01; 95% CI: 1.00-1.02; P = 0.005), Hospital (1.03; 95% CI: 1.01-1.05), P < 0.001) and making more visits to the hospital accident and emergency department (OR: 1.12, 95% CI: 1.07-1.17). Conclusions: almost a quarter of patients followed up in a care continuity unit between internal and family medicine presented multiple pathologies and a third presented comorbidity. Multiple pathologies and comorbidity are predictive characteristics of hospital admission together with a greater use of health services.(AU)

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.