Systemic Anticoagulation and Inpatient Outcomes of Pancreatic Cancer: Real-World Evidence from U.S. Nationwide Inpatient Sample

Abstract

Background: Pancreatic cancer can induce a hypercoagulable state which may lead to clinically apparent thrombosis. However, the effect of anticoagulants remains ambiguous. This study aimed to investigate the potential effect of long-term systemic anticoagulant usage on hospitalization outcomes of patients with pancreatic cancer. Methods: This retrospective study extracted all data from the U.S. Nationwide Inpatient Sample (NIS) database from 2005 to 2018. We included hospitalized adults ≥18 years old with a pancreatic cancer diagnosis identified by International Classification of Diseases ninth revision (ICD-9) and tenth revision (ICD-10) codes. We utilized diagnostic codes ICD9 V58.61 and ICD10 Z79.01, i.e., ‘long-term use of anticoagulant’, to identify individuals who were on a long-term systemic anticoagulant. The study cohort were then further grouped as being with or without long-term systemic use of an anticoagulant. Propensity score matching was performed to balance the characteristics of the two groups. The risks of life-threatening events, e.g., acute myocardial infarction (AMI), acute heart failure (AHF), sepsis, shock, and acute kidney injury (AKI), in-hospital death, and prolonged length of stay (LOS) in the hospital were compared between the groups by univariable and multivariable logistic regression analyses. Results: The study population consisted of 242,903 hospitalized patients with pancreas cancer, 6.5% (n = 15,719) of whom were on long-term systemic anticoagulants. A multivariable regression analysis showed that long-term systemic anticoagulant use was independently associated with lower odds of sepsis (aOR: 0.81, 95% CI: 0.76–0.85), shock (aOR: 0.59, 95% CI: 0.51–0.68), AKI (aOR: 0.86, 95% CI: 0.81–0.91), in-hospital mortality (aOR: 0.65, 95% CI: 0.60–0.70), and prolonged LOS (aOR: 0.84, 95% CI: 0.80–0.89). Conclusions: Long-term systemic anticoagulant use is associated with better clinical outcomes in terms of decreased risks of some life-threatening events, in-hospital death, and prolonged LOS among hospitalized patients with pancreatic cancer in the U.S.

The impact of rheumatoid arthritis on perioperative complications and hospitalization costs of lumbar fusion: a national analysis

“…This study has five methodological limitations ( 34 – 36 ): (1) The sample derived from the US NIS database represents predominantly North American populations, whose demographic, socioeconomic, and insurance characteristics may limit the generalizability of findings to other healthcare systems. (2) Although we restricted the analysis to elective, primary, degenerative lumbar fusion, ICD-9/10 codes cannot reliably distinguish pathological subtypes such as isthmic vs. degenerative spondylolisthesis, introducing potential misclassification that may reduce internal validity.…”

The impact of rheumatoid arthritis on perioperative complications and hospitalization costs of lumbar fusion: a national analysis

“…This study has five methodological limitations ( 34 – 36 ): (1) The sample derived from the US NIS database represents predominantly North American populations, whose demographic, socioeconomic, and insurance characteristics may limit the generalizability of findings to other healthcare systems. (2) Although we restricted the analysis to elective, primary, degenerative lumbar fusion, ICD-9/10 codes cannot reliably distinguish pathological subtypes such as isthmic vs. degenerative spondylolisthesis, introducing potential misclassification that may reduce internal validity.…”

Protective effects of long term antiplatelet and anticoagulant therapy in hospitalized patients with inflammatory bowel disease

“…NIS being an administrative database, has a potential for misclassification of disease using ICD-10 codes or missing codes. However, previous studies have used similar ICD-10 codes to obtain information regarding long term use of antiplatelet/anticoagulant agents used[ 9 , 12 , 13 ]. The ICD-10 codes were used to query for long term antiplatelets/anticoagulant agents use.…”

“…Patients taking long term anticoagulants/antiplatelets on daily outpatient basis were identified using appropriate ICD-10 codes. ICD-10 code Z7902 and Z7982 were used to identify patients on long term antiplatelet agents whereas ICD-10 code Z7901 was used to identify patients on long term anticoagulant agents[ 9 , 12 , 13 ]. Combination of ICD-10 codes for patients on long term antiplatelet agents and anticoagulant agents were used to query the patient subgroup on both antiplatelet and anticoagulant agents.…”

Adverse complications of cervical spinal fusion in patients with different types of diabetes mellitus: a retrospective nationwide inpatient sample database cross-sectional study

“…Selected variables for PSM were all variables 15 supplied by the NIS including age group, sex, race, income, type of insurance, type of admission, bed size of hospital, teaching status of hospital, location of hospital, region of hospital, the level and the approach of fusion, alcohol abuse, deficiency anemia, rheumatoid diseases, chronic blood loss anemia, congestive heart failure, chronic pulmonary disease, coagulopathy, depression, drug abuse, hypothyroidism, liver disease, fluid and electrolyte disorders, neurological disorders, obesity, paralysis, peripheral vascular disorders, psychoses, pulmonary circulation disorders, renal failure, solid tumor without metastasis, and weight loss (Supplemental Table 1, Supplemental Digital Content 2, http://links.lww.com/JS9/D272 ). Variables that remained significantly different between the study groups after matching were adjusted in the multivariable analysis 31 .…”

“…Before propensity score matching, the first multivariable logistic regression analysis was adjusted for all variables supplied by the NIS (Supplemental Tables 1–3, Supplemental Digital Content 2, http://links.lww.com/JS9/D272 ) before PSM 15 . After propensity score matching, multivariable logistic regression analysis was adjusted for variables that remained significantly different between the study groups after PSM (Tables 7 and 8 ) 31 .…”

The impact of rheumatoid arthritis on perioperative complications and hospitalization costs of lumbar fusion: a national analysis

“…This study has five methodological limitations ( 34 – 36 ): (1) The sample derived from the US NIS database represents predominantly North American populations, whose demographic, socioeconomic, and insurance characteristics may limit the generalizability of findings to other healthcare systems. (2) Although we restricted the analysis to elective, primary, degenerative lumbar fusion, ICD-9/10 codes cannot reliably distinguish pathological subtypes such as isthmic vs. degenerative spondylolisthesis, introducing potential misclassification that may reduce internal validity.…”

Protective effects of long term antiplatelet and anticoagulant therapy in hospitalized patients with inflammatory bowel disease

“…NIS being an administrative database, has a potential for misclassification of disease using ICD-10 codes or missing codes. However, previous studies have used similar ICD-10 codes to obtain information regarding long term use of antiplatelet/anticoagulant agents used[ 9 , 12 , 13 ]. The ICD-10 codes were used to query for long term antiplatelets/anticoagulant agents use.…”

“…Patients taking long term anticoagulants/antiplatelets on daily outpatient basis were identified using appropriate ICD-10 codes. ICD-10 code Z7902 and Z7982 were used to identify patients on long term antiplatelet agents whereas ICD-10 code Z7901 was used to identify patients on long term anticoagulant agents[ 9 , 12 , 13 ]. Combination of ICD-10 codes for patients on long term antiplatelet agents and anticoagulant agents were used to query the patient subgroup on both antiplatelet and anticoagulant agents.…”

Adverse complications of cervical spinal fusion in patients with different types of diabetes mellitus: a retrospective nationwide inpatient sample database cross-sectional study

“…Selected variables for PSM were all variables 15 supplied by the NIS including age group, sex, race, income, type of insurance, type of admission, bed size of hospital, teaching status of hospital, location of hospital, region of hospital, the level and the approach of fusion, alcohol abuse, deficiency anemia, rheumatoid diseases, chronic blood loss anemia, congestive heart failure, chronic pulmonary disease, coagulopathy, depression, drug abuse, hypothyroidism, liver disease, fluid and electrolyte disorders, neurological disorders, obesity, paralysis, peripheral vascular disorders, psychoses, pulmonary circulation disorders, renal failure, solid tumor without metastasis, and weight loss (Supplemental Table 1, Supplemental Digital Content 2, http://links.lww.com/JS9/D272 ). Variables that remained significantly different between the study groups after matching were adjusted in the multivariable analysis 31 .…”

“…Before propensity score matching, the first multivariable logistic regression analysis was adjusted for all variables supplied by the NIS (Supplemental Tables 1–3, Supplemental Digital Content 2, http://links.lww.com/JS9/D272 ) before PSM 15 . After propensity score matching, multivariable logistic regression analysis was adjusted for variables that remained significantly different between the study groups after PSM (Tables 7 and 8 ) 31 .…”

The impact of rheumatoid arthritis on perioperative complications and hospitalization costs of lumbar fusion: a national analysis

“…This study has five methodological limitations ( 34 – 36 ): (1) The sample derived from the US NIS database represents predominantly North American populations, whose demographic, socioeconomic, and insurance characteristics may limit the generalizability of findings to other healthcare systems. (2) Although we restricted the analysis to elective, primary, degenerative lumbar fusion, ICD-9/10 codes cannot reliably distinguish pathological subtypes such as isthmic vs. degenerative spondylolisthesis, introducing potential misclassification that may reduce internal validity.…”

Protective effects of long term antiplatelet and anticoagulant therapy in hospitalized patients with inflammatory bowel disease

“…NIS being an administrative database, has a potential for misclassification of disease using ICD-10 codes or missing codes. However, previous studies have used similar ICD-10 codes to obtain information regarding long term use of antiplatelet/anticoagulant agents used[ 9 , 12 , 13 ]. The ICD-10 codes were used to query for long term antiplatelets/anticoagulant agents use.…”

“…Patients taking long term anticoagulants/antiplatelets on daily outpatient basis were identified using appropriate ICD-10 codes. ICD-10 code Z7902 and Z7982 were used to identify patients on long term antiplatelet agents whereas ICD-10 code Z7901 was used to identify patients on long term anticoagulant agents[ 9 , 12 , 13 ]. Combination of ICD-10 codes for patients on long term antiplatelet agents and anticoagulant agents were used to query the patient subgroup on both antiplatelet and anticoagulant agents.…”

Adverse complications of cervical spinal fusion in patients with different types of diabetes mellitus: a retrospective nationwide inpatient sample database cross-sectional study

“…Selected variables for PSM were all variables 15 supplied by the NIS including age group, sex, race, income, type of insurance, type of admission, bed size of hospital, teaching status of hospital, location of hospital, region of hospital, the level and the approach of fusion, alcohol abuse, deficiency anemia, rheumatoid diseases, chronic blood loss anemia, congestive heart failure, chronic pulmonary disease, coagulopathy, depression, drug abuse, hypothyroidism, liver disease, fluid and electrolyte disorders, neurological disorders, obesity, paralysis, peripheral vascular disorders, psychoses, pulmonary circulation disorders, renal failure, solid tumor without metastasis, and weight loss (Supplemental Table 1, Supplemental Digital Content 2, http://links.lww.com/JS9/D272 ). Variables that remained significantly different between the study groups after matching were adjusted in the multivariable analysis 31 .…”

“…Before propensity score matching, the first multivariable logistic regression analysis was adjusted for all variables supplied by the NIS (Supplemental Tables 1–3, Supplemental Digital Content 2, http://links.lww.com/JS9/D272 ) before PSM 15 . After propensity score matching, multivariable logistic regression analysis was adjusted for variables that remained significantly different between the study groups after PSM (Tables 7 and 8 ) 31 .…”