Outcome and Determining Characteristics of ICU Patients with Acute Kidney Injury in a Low-Income Country, a Multicenter Experience

Variables	Chi-square	df	P-value
Age	26.980	1	0.001
Gender	0.150	1	0.698
BMI	95.265	2	< 0.001
APACHE	350.519	2	< 0.001
KDIGO class	93.712	3	< 0.001
Admitting ward	93.009	3	< 0.001
Co-morbidities	72.626	1	< 0.001
Source of AKI	0.074	1	0.785
Length of stay	16.588	2	< 0.001
Interventions	69.377	6	< 0.001
Bill payment	31.988	2	< 0.001

[i] Clinical data of sampled patients indicating demographic profile (Age, Gender, BMI) clinical state (APACHE & KDIGO), co-morbidity, source of AKI (hospital-acquired or community-acquired), length of ICU stay, intervention rendered while in ICU (renal replacement therapy RRT, Vasopressors, Mechanical ventilation MV), bill payment (either out of pocket expenses, through the National health insurance scheme or the Social welfare services)

Table 2.

Logistic regression table

Variables	Odd ratio	95% CI lower	95% CI upper	p-value
Age
< 50 years	0.550	0.140	2.165	0.392
> 50 years		-	-	-
Gender
Male	0.746	0.029	19.389	0.782
Female	-	-	-	-
BMI
Underweight	0.746	0.029	19.386	0.860
Normal weight	0.000	0.000	-	0.997
Obese	-	-	-	-
APACHEII
Mild	0.032	0.001	0.757	0.033
Moderate	-	0.000	0.002	< 0.001
Severe	-	-	-	-
Classification
KDIGO1	9.087	0.558	148.023	0.121
KDIGO 2	1.144	0.060	21.693	0.929
KDIGO 3	-	-	-	-
Source of AKI
Community	12.031	0.102	2.587	0.420
Hospital	-	-	-	-
Admitting ward
Obs&Gyn	0.143	0.007	2.917	0.206
Medical	-	0.000	-	0.997
Surgical	0.133	0.006	2.899	0.199
Emergency	-	-	-	-
Length of stay
≤ 7 days	0.249	0.007	8.716	0.444
8-14 days	0.000	0.000	-	0.999
> 14 days	-	-	-	-
Comorbidities
Yes	12.031	0.031	0.102	2.587
No	-	-	-	-
Interventions
RRT	0.000	0.000	-	0.995
Vasopressors	3.305	0.007	1501.012	0.702
MV	1.115	0.061	20.263	0.941
RRT + Vasopressors	1.403	0.109	18.047	0.795
RRT + MV	1.546	0.197	12.144	0.678
RRT + Vasopressors + MV	0.001	0.000	0.059	< 0.001
RRT + Vasopressors	-	-	-	-
Bill payment source
Out of pocket	7.050	0.009	5554.736	0.566
Insurance scheme	2.972	0.147	59.937	0.477
Social welfare	-	-	-	-

[i] Logistic regression analysis to determine the predictors of patients’ outcomes admitted in the ICU

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DOI: https://doi.org/10.2478/jccm-2024-0037 | Journal eISSN: 2393-1817 | Journal ISSN: 2393-1809

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Language: English

Page range: 345 - 352

Submitted on: Apr 10, 2024

Accepted on: Aug 20, 2024

Published on: Oct 31, 2024

Published by: University of Medicine, Pharmacy, Science and Technology of Targu Mures

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

acute kidney injury,

intensive care unit,

mortality,

KDIGO stratification,

Related subjects:

Internal medicine, other,

Surgery,

Surgery, other,

Anaesthesiology,

Emergency medicine and intensive-care medicine

© 2024 Abubakar Ballah, Jika Mohammed, Gyadale Abdulhamid Njidda, Halima Bidemi Yahaya, Nwokorie Mabong Rosemary, Ibrahim Naziru, Adamu Yusuf Baffah, Hassan Ali Maina, Hajara Galadima, Abdullahi Maryam Mohammed, Franklin Andibanbang, Adam Ibrahim Abdullahi, published by University of Medicine, Pharmacy, Science and Technology of Targu Mures
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 10 (2024): Issue 4 (October 2024)