::ReSECT - Scientific Report

Recruitment

Note

Enrolment figures refer to the date of creation of each participation entity (patient, personal registry and procedure in the anatomical lung resection process) in the data platform.

Cumulative Recruitment

Code

a_datosClinicos <- readRDS("../participacion/actividad/a_datosClinicos.RDS") # patient level
a_regPersonal <- readRDS("../participacion/actividad/a_regPersonal.RDS")     # op-log level
a_rpaPreop <- readRDS("../participacion/actividad/a_rpaPreop.RDS")           # RPA level


reclutamiento_patient <- a_datosClinicos %>%
  distinct(id, .keep_all = TRUE) %>%
  mutate(creation = as.Date(creation_time)) %>%
  filter(creation > "2023-01-01") %>%
  mutate(week = week(creation)) %>%
  count(creation) %>%
  arrange(creation) %>%
  mutate(acum = cumsum(n),
         level = "Patients")

reclutamiento_regPersonal <- a_regPersonal %>%
  distinct(id, .keep_all = TRUE) %>%
  mutate(creation = as.Date(creation_time)) %>%
  filter(creation > "2023-01-01") %>%
  mutate(week = week(creation)) %>%
  count(creation) %>%
  arrange(creation) %>%
  mutate(acum = cumsum(n),
         level = "Personal Registries")

reclutamiento_rpa <- a_rpaPreop %>%
  distinct(id, .keep_all = TRUE) %>%
  mutate(creation = as.Date(creation_time)) %>%
  filter(creation > "2023-01-01") %>%
  mutate(week = week(creation)) %>%
  count(creation) %>%
  arrange(creation) %>%
  mutate(acum = cumsum(n),
         level = "ALR-Process Procedures")

reclutamiento <- bind_rows(reclutamiento_patient, reclutamiento_regPersonal,reclutamiento_rpa)

reclutamiento %>%
  ggplot(aes(x = creation, y = acum, color= factor(level, levels=c("Personal Registries", "Patients", "ALR-Process Procedures")))) +
  geom_line(size = 1.2) +
  scale_x_date(breaks = "3 month", date_labels = "%b-%y")+
  scale_color_manual(values = azul5) +
  labs(x=NULL, y=NULL, color = "Level") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle=30),
        plot.caption = element_text(face="italic"),
        legend.position = c(0.2,0.7))

Recruitment per Year

Code

n_year(level = "patient", path = "../participacion/actividad/a_datosClinicos.RDS")

Code

n_year(level = "operativelog", path = "../participacion/actividad/a_datosClinicos.RDS")

Code

n_year(level = "process", path = "../participacion/actividad/a_datosClinicos.RDS")

Recruitment per Quarter

Code

n_quarter(level = "patient", path = "../participacion/actividad/a_datosClinicos.RDS")

Code

n_quarter(level = "operativelog", path = "../participacion/actividad/a_datosClinicos.RDS")

Code

n_quarter(level = "process", path = "../participacion/actividad/a_datosClinicos.RDS")

Operative Log

Note

Operative-Log figures refer to the date of surgery of each record in the data platform.

Due to the operative-log functionality of the personal records form, a single procedure in a given patient may have several personal records, leading to an undesirable multiplicity of data for the purpose of population analysis. To overcome this situation, we have opted to filter the multiple personal records for the same procedure based on a chronological criterion. Therefore, we have kept the earliest record to build the following plot.

Type of Procedures

Number of unique personal records: 4936

Code

n_oplog()

Code

n_oplog(2023)

Code

n_oplog(2024)

Anatomical Lung Resection Process

Total number of procedures: 2768

The number of procedures per Department can be consulted in the Centre Activity section.

Demographics

Code

datosClinicos_rpa$sexo <- factor(datosClinicos_rpa$sexo,
       levels = c("Hombre", "Mujer"),
       labels = c("Male", "Female"))


p1 <- datosClinicos_rpa %>%
  filter(!is.na(sexo)) %>%
  count(sexo) %>% mutate(percent = n/sum(n)) %>%
  ggplot (aes(x = sexo, y = percent)) +
  geom_col(aes(fill = sexo), width = 0.6) +
  geom_label(aes(label=n), size=3) +
  scale_fill_manual (values = c(color1, color2)) +
  scale_y_continuous(label = scales::percent)+
  theme_minimal() +
  theme(
    legend.position = "none") +
  labs(y = "Pacientes\n", x = NULL)


p2 <- datosClinicos_rpa %>% 
  filter(edad_del_paciente_a_la_fecha_de_intervencion < 100, !is.na(sexo)) %>%
  ggplot (aes(x = edad_del_paciente_a_la_fecha_de_intervencion, fill = sexo)) +
  geom_histogram(color = "white", position = "dodge") +
  scale_fill_manual(values = c(color1, color2))+
  scale_y_continuous(position = "right")+
  theme_minimal() +
  labs(x = NULL, y = NULL, fill = NULL)



plot_grid(p1, p2, nrow = 1, rel_widths = c(1/4, 3/4))

Code

datosClinicos_rpa %>%
  filter(!is.na(sexo)) %>%
  ggplot(aes(x = fct_rev(fct_infreq(factor(hospital))), fill = as.factor(sexo))) +
  geom_bar(width = 0.6, position = "fill") +
  scale_y_continuous(breaks= pretty_breaks(), labels=scales::percent) +
  scale_fill_manual(values = c(color1, color2)) +
  coord_flip() +
  theme_minimal() +
  theme(
    plot.caption = element_text(hjust = -2, face = "italic"),
    axis.text.y = element_text(size = 7))+
  labs(x = NULL, y = NULL, fill = NULL, caption = "Centres ordered by number of procedures")

Code

datosClinicos_rpa %>% 
  filter(edad_del_paciente_a_la_fecha_de_intervencion < 100) %>%
  ggplot(aes(x = reorder(hospital, edad_del_paciente_a_la_fecha_de_intervencion, FUN=median) , y =      edad_del_paciente_a_la_fecha_de_intervencion)) +
  geom_boxplot(fill = color2) +
  stat_summary(fun.y=mean, geom="point", shape=20, size=3, color= "orange") +
  coord_flip() +
  theme_minimal() +
  theme(
    plot.caption = element_text(hjust = -0.7, face = "italic"),
    axis.text.y = element_text(size = 7))+
  labs(x = NULL, y = "\nAge", caption = "Centres ordered by median age")

Type of Lung Resection

Code

rpa_type(type = c("procedimiento_pulmonar"))

Code

rpa_type(2023, type = c("procedimiento_pulmonar"))

Code

rpa_type(2024, type = c("procedimiento_pulmonar"))

Type of Surgical Approach

Code

rpa_type(type = "abordaje_final")

Code

rpa_type(2023, "abordaje_final")

Code

rpa_type(2024, "abordaje_final")

Lung Resection and Surgical Approach

Code

rpa_type(type = c("procedimiento_pulmonar", "abordaje_final"))

Code

rpa_type(2023, type = c("procedimiento_pulmonar", "abordaje_final"))

Code

rpa_type(2024, type = c("procedimiento_pulmonar", "abordaje_final"))

Complications

The percentage of patients with some type of complication recorded prior to discharge from hospital is 28.1 % for all the Registry. The distribution of complications according to their degree of severity is as follows:

Code

rpa_complications()

Code

rpa_complications(2023)

Code

rpa_complications(2024)

Postop. Stay and Type of Resection

Code

rpa_stay(type="procedimiento_pulmonar")

Code

rpa_stay(2023, type="procedimiento_pulmonar")

Code

rpa_stay(2024, type = "procedimiento_pulmonar")

Postop Stay and Surgical Approach

Code

rpa_stay(type = "abordaje_final")

Code

rpa_stay(2023, type = "abordaje_final")

Code

rpa_stay(2024, type = "abordaje_final")

Diagnosis

Code

datosClinicos_rpa$grupo_diagnostico2 <- fct_recode(
  datosClinicos_rpa$grupo_diagnostico, 
  "Primary Malignant" = "Tumoral Maligno Primario",
  "Secondary Malignant" = "Tumoral Maligno Secundario",
  "Benign Tumour" = "Tumoral Benigno",
  "Inflammatory Disease" = "Inflamatorio - Infeccioso",
  "Trauma" = "Traumático", 
  "Bullous Disease" = "Enfisema Bulloso")

datosClinicos_rpa$grupo_diagnostico2 <- fct_explicit_na(datosClinicos_rpa$grupo_diagnostico2)

datosClinicos_rpa |>
  mutate(dx = fct_lump_n(grupo_diagnostico2, 4)) |> 
  count(dx) |> 
  mutate(percent = round(100*n/sum(n),1)) |>
  arrange(n) |>
  ggplot(aes(x = reorder(dx,n), y=n, label = paste(percent, "%"))) +
  geom_col(fill=color1, width = 0.7)+
  geom_label()+
  labs(x=NULL, y=NULL)+
  theme_minimal()+
  coord_flip()

The following charts on staging refer to the group of patients with primary malignant lung tumors that are not compatible with recurrence of a previous tumor.

Clinical Stage

Code

rpa_estadio(type = "estadio_clinico")

Code

rpa_estadio(2023, "estadio_clinico")

Code

rpa_estadio(2024, "estadio_clinico")

Pathological Stage

Code

rpa_estadio(type = "estadio_patologico")

Code

rpa_estadio(2023, "estadio_patologico")

Code

rpa_estadio(2024, "estadio_patologico")

Clinical vs Pathological Stage

Code

rpa_estadio(type = c("estadio_clinico", "estadio_patologico"))