A perilous and complicated situation in hospitals: Vitamin D deficiency in multiple myeloma patients—A nationwide cross-sectional study.

person Manisha Rakesh Lakhanpal Emilio Aguinaldo College, Manila, Philippines info_outline Manisha Rakesh Lakhanpal, Nikhila Chelikam, Divesh Manjani, Simmy Lahori, Sai Anusha Akella, Prashanth Gumpu Shivashankar, Shlok Vimal Shah, Abdirazak Ali, Varneet Toor, Aryak Singh, Rahul Gujarathi, Lokesh Manjani, Urvish Patel, Avinash Adiga, Geetika Kukreja

Authors person Manisha Rakesh Lakhanpal Emilio Aguinaldo College, Manila, Philippines info_outline Manisha Rakesh Lakhanpal, Nikhila Chelikam, Divesh Manjani, Simmy Lahori, Sai Anusha Akella, Prashanth Gumpu Shivashankar, Shlok Vimal Shah, Abdirazak Ali, Varneet Toor, Aryak Singh, Rahul Gujarathi, Lokesh Manjani, Urvish Patel, Avinash Adiga, Geetika Kukreja Organizations Emilio Aguinaldo College, Manila, Philippines, Icahn School of Medicine at Mount Sinai, New York, NY, University of medicine and pharmacy, Timisoara, Romania, Timisoara, Romania, Mayo Clinic, Rochester, MN, KIMS Hospital, Secunderabad, India, Adichunchanagiri Hospital, Bangalore, India, Civil Hospital Asarwa, Ahmedabad, India, University of Minnesota, Minnesota, MN, Agartala Government Medical College & Govind Ballabh Pant Hospital, Agartala, India, Sir Seewoosagur Ramgoolam Medical College, Belle Rive, Mauritius, University of Florida Health, Jacksonville, FL, MedStar Washington Hospital Center, Washington, DC, Huntsville Hospital, Huntsville, AL, Division of Hematology-Oncology, Henry Ford Cancer Institute Macomb, Shelby Township, MI Abstract Disclosures Research Funding No funding received None. Background: For the year 2019, the United States age-adjusted rate of new Multiple Myeloma (MM) cases was 6.8 per 100,000 people and the age-adjusted death rate was 3 per 100,000 people. Vitamin D Deficiency (VDD) and associated complications are very common in MM patients. Aim of our study was to evaluate the prevalence and risk of VDD in patients with MM and VDD associated disability and mortality in the VDD patients. Methods: We performed a cross-sectional retrospective analysis of Nationwide Inpatient Sample data 2016-2018. Adults hospitalizations with MM were identified using ICD 10 code. Hospitalized patients were grouped into patients with VDD and without VDD. We ran chi square, unpaired-t test and mixed-effect survey logistic regression analysis to find out odds ratio and 95%CI showing outcomes of VDD in MM patients. Results: We found 330,175 hospitalizations with MM. Of these 11,480 were noted to be patients with VDD with higher prevalence (0.64% vs 0.37%) of and strong association (aOR:1.41, 95%CI:1.35-1.47) with MM in comparison without MM. Amongst MM, females [1.50, 1.49-1.51], black race [1.23, 1.22-1.24], urban teaching hospitals [1.22, 1.22-1.23], and hospitals in mid-west region [1.30, 1.29- 1.32] had higher prevalence odds of VDD compared with males, white race population, rural hospitals and hospitals in northeast region. (p<0.0001) Patients with VDD had higher prevalence and strong association with higher morbidity [5.1% vs 3.9%, 1.24,1.14-1.36; c-value=0.67], severe/extreme disability [77.2% vs 73.3%, 1.26, 1.20-1.33; c-value=0.73], and discharge to locations other than home [51.15% vs 45.69%, 1.29, 1.23-1.34; c-value=0.69] in comparison without VDD. (p<0.0001) [Table]. Conclusions: Significant poor outcomes warrant early identification of VDD and role of same hospitalization vitamin D replacement amongst MM patients with higher risk. Regression analysis showing association between vitamin D deficiency and poor outcomes. Outcomes Adjusted odds ratio (aOR) 95% Confidence interval (95%CI) p-value c-value Morbidity* 1.24 1.14 - 1.36 <.0001 0.667 Major/Extreme disability 1.26 1.20 - 1.33 <.0001 0.725 Discharge other than home # 1.28 1.23 - 1.34 <.0001 0.693 * Morbidity: Length stay of ≥17 days (90th percentile) + discharge other than home. # Discharge other than home: discharge to short term hospital, skilled nursing facility, or intermediate care facility. Models are adjusted for patients’ demographics (age, sex, race, income category, primary payer, and procedure days and type), hospital level characteristics (hospital size, location, region, and teaching status), patients’ comorbidity (hypertension, diabetes, obesity, hypercholesterolemia, drug, alcohol and tobacco abuse, alcohol withdrawal, HIV, renal dysfunction, and ischemic heart disease), and Charlson Comorbidity Index.