Development of a mortality score to assess risk of adverse drug reactions among hospitalized patients with moderate to severe chronic kidney disease

Abstract

Background Chronic kidney disease (CKD) is a significant health burden that increases the risk of adverse events. Currently, there is no validated models to predict risk of mortality among CKD patients experienced adverse drug reactions (ADRs) during hospitalization. This study aimed to develop a mortality risk prediction model among hospitalized CKD patients whom experienced ADRs. Methods Patients data with CKD stages 3–5 admitted at various wards were included in the model development. The data collected included demographic characteristics, comorbid conditions, laboratory tests and types of medicines taken. Sequential series of logistic regression models using mortality as the dependent variable were developed. Bootstrapping method was used to evaluate the model’s internal validation. Variables odd ratio (OR) of the best model were used to calculate the predictive capacity of the risk scores using the area under the curve (AUC). Results The best prediction model included comorbidities heart disease, dyslipidaemia and electrolyte imbalance; psychotic agents; creatinine kinase; number of total medication use; and conservative management (Hosmer and Lemeshow test =0.643). Model performance was relatively modest (R square = 0.399) and AUC which determines the risk score’s ability to predict mortality associated with ADRs was 0.789 (95% CI, 0.700–0.878). Creatinine kinase, followed by psychotic agents and electrolyte disorder, was most strongly associated with mortality after ADRs during hospitalization. This model correctly predicts 71.4% of all mortality pertaining to ADRs (sensitivity) and with specificity of 77.3%. Conclusion Mortality prediction model among hospitalized stages 3 to 5 CKD patients experienced ADR was developed in this study. This prediction model adds new knowledge to the healthcare system despite its modest performance coupled with its high sensitivity and specificity. This tool is clinically useful and effective in identifying potential CKD patients at high risk of ADR-related mortality during hospitalization using routinely performed clinical data.

Prediction of mortality among patients with chronic kidney disease: A systematic review

“…Non-dialysis population: Several predictive factors for mortality have been identified among non-dialysis CKD participants. Urine protein creatinine ratio (UPCR), eGFR, mortality risk score and malnutrition inflammation score (MIS) have been demonstrated to be predictive of mortality in this patient population[ 28 , 30 , 31 ]. It is thus not surprising that Wang et al [ 21 ] reported that UPCR and eGFR are predictive of mortality among CKD patients.…”

“…In 2019, Danial et al [ 30 ] developed a mortality risk score for CKD patients. In this study, the authors investigated mortality from adverse drug reactions (ADRs) that specifically occurred in CKD patients.…”

“…In this study, the authors investigated mortality from adverse drug reactions (ADRs) that specifically occurred in CKD patients. The mortality risk prediction model included the following variables: (1) History of heart disease; (2) Dyslipidemia; (3) Electrolyte imbalance; (4) Psychotic agents; (5) Creatine kinase; (6) Total number of medications; and (7) Conservative management (how the treatment was provided to subjects with ADR)[ 30 ]. The authors noted that these clinical factors are easily obtained in clinical practice, and this score allowed preemptive identification of CKD patients at risk of ADR-associated mortality.…”

Drug-Related Problems in Hospitalised Patients with Chronic Kidney Disease: A Systematic Review

“…A 2018 Malaysian hospital study found that 17.5% of hospitalised CKD participants died after an ADR, with the degree of renal impairment significantly associated with mortality after ADR (Danial et al, 2019). Previous reviews have focused on hospitalisation due to medication problems in the general population (Nivya et al, 2015, Patel et al, 2017, with one review specifically focused on medication problems in non-hospital renal patients (Dorks et al, 2017).…”

Development of Screening Tools to Predict Medication-Related Problems Across the Continuum of Emergency Department Care: A Prospective, Multicenter Study

“…To identify weighted scores for screening tool predictor variables, beta-coefficients from the multivariable regression were multiplied by ten and rounded to the nearest whole number in a method used by Moore ( Moore et al, 2012 ). Internal validation of the models was undertaken using bootstrapping of 1,000 resamples to assess reliability of the coefficients of regression ( Danial et al, 2019 ). Standard errors were used to calculate the 95% bootstrap confidence intervals of the odds ratios.…”