This is the first prognostic research on LTFU among HIV clients on ART. This study revealed the incidence of LTFU was 11.19 (95% CI 8.95–13.99) per 100-person years. One in 4 patients (25.69%) had LTFU from the treatment in the current study. When compared to findings elsewhere in Ethiopia, the LTFU rate in the present study was higher than findings from Aksum [9], Debre Markos [17], Mizan Teferi [24]. It was similar to results from Gondar [21, 25], South Ethiopia [22], Hadiya [27] but lower than the findings from Eastern Ethiopia (Jigiga) [11]. In addition, this rate was lower than findings from studies conducted in South Africa [36], Malawi [37], and Guinea-Bissau [38]. The dissimilarity in measurement [39], access to HIV care services, innovation, adoption of new strategies like the universal test and treat approach [40], and difference in year of study could be the possible reasons for variations in rates of LTFU.
In previous years, the focus of the research was to explain the incidence and factors associated with a certain outcome. But in recent years, the emphasis is shifted to predicting the risk using a combined set of characteristics. In our study, a combination of six prognostic determinants (prophylaxis status, ASM status, HVL status, adherence level, residence, BMI status) results in an AUROC of 0.86 (95% CI 0.8–0.9), which is good accuracy according to diagnostic accuracy classification [41, 42]. Having an AUROC of 0.86 (95% CI 0.8–0.9) means that the model is 86% accurate in discriminating between a randomly selected subject who was lost from a randomly selected subject who was not lost from care.
Among the prognostic determinants, HVL status alone has the highest AUC value which is 0.76% (95% CI 0.72–0.81%) followed by prophylaxis and adherence status with an AUC value of 0.71% (95% CI 0.66–0.76%) and 0.71% (95% CI 0.66–0.76%), respectively. Other’s determinants have low predictive value which is less than 0.70% [ASM status 0.59% (95% CI 0.56–0.62%), normal BMI 0.55% (95% CI 0.56–0.62%), and residence 0.53% (95% CI 0.50–0.60%)].
Though both the regression formula and risk score chart have good accuracy, the AUROC from the regression formula is slightly higher than that of the risk score 0.86 (95% CI 0.8-0.9) vs 0.81 (95% CI 0.77–0.85). Thus, using the regression formula to predict LTFU is better and advisable. The model has also a good calibration with a p-value of 0.350. Good calibration means that the estimated probability of LTFU using the model is similar to the observed LTFU frequency. A statistically significant (p < 0.05) test indicates marked differences between predicted probabilities and observed once and thus poor calibration.
As shown in Fig. 3, the model has the highest net benefit across the entire range of threshold probabilities, which indicates that the model has the highest clinical and public health value. Hence, using the model for the prediction of LTFU has a higher net benefit than not using it. Prognostic research aims to find a risk prediction tool that is simple to use, accurate in predicting risk, generalizable across contexts, and uses routinely collected determinants that are needed to identify patients at high risk for poor outcomes and to provide individualized risk assessment [32]. Thus, clinicians can also use the developed risk score chart for the prediction of LTFU among ART patients as it is simple and has good prediction accuracy (AUC = 81%).
As a result, the overall risk score for the risk prediction tool based on the score chart is 8, and the risk of LTFU grows as the risk score increases. We categorized the cohort into two risk groups in addition to predicting the degree of LTFU risk associated with each risk score. When compared to the low-risk group (risk score less than 3), those in the high-risk category (risk score greater than or equal to 3) had a fourfold (OR 3.68; % CI 1.69–5.66) increased risk of LTFU.
Depending on the availability of resources, health care providers can use different cutoff points. If the providers value sensitivity and specificity equally, the risk score’s cutoff value of 3 maximized the value of both sensitivity and specificity (86% and 64%, respectively). The positive and negative predictive values, respectively, were 45% and 93%. However, health care providers may choose to utilize different cutoff points depending on the importance of false positives and false negatives. A lower risk score cutoff value would target a substantial section of our population for intervention and identify the majority of people who were lost to therapy.
Patients who do not take prophylaxis were found to be at higher risk of LTFU. This was consistent with other studies [21, 24, 25, 27, 28, 31]. This is because of the direct effect of isoniazid in preventing active tuberculosis, which in turn improves the quality of life of patients, which leads to a longer stay in the treatment [21, 25, 27]. The exiting intervention such as management and prevention of opportunistic infections like pneumocystis pneumonia (PCP), toxoplasmosis, bacterial infections and diarrheal diseases through providing prophylaxis like CPT could encourage patients to be engaged and could bring the effort to retain patients from the start of HIV treatment [6, 21, 43].
Patients with suboptimal adherence were at an increased risk of being LTFU when compared with those with exemplary commitment. This was supported by other studies [8, 25, 27]. The possible reason could be patients with suboptimal adherence may have socio-demographic and clinical problems that affect their adherence initially, which further affect retention in care [44]. In addition, patients with suboptimal adherence are at a higher risk of treatment failure, which makes them to be more vulnerable to many opportunistic diseases, with higher chance to have more pill burden, adverse drug toxicities, and interactions among opportunistic infection treatment and ART, which demands a high level of commitment to follow all those medications [44, 45].
This study revealed that rural residents were found to be more likely to be LTFU in the treatment as compared to their counterparts. Studies evidenced that travel time to the clinics and its opportunity costs (in terms of financial cost or time allocated to something else), level of patient’s awareness of the treatment, and social stigma are significant barriers to patient adherence to ART and maintenance in care [18, 37].
Contrary to previous evidences [37, 38, 46], patients who had a normal baseline BMI were about three times more likely to be LTFU in treatment compared to those patients who had low BMI. This may be due to the reason that patients who had a normal baseline BMI may feel that they are well and their health-seeking behavior may be inadequate and patients with low BMI at ART initiation were probably more symptomatic and had counseled about good adherence in the lifelong follow-up treatments, which may have resulted in greater motivation to remain in care [47].
In our study, patients on ASM are at a higher risk of LTFU than patients not on ASM. This may be due to the problem with lower potency of the drug, which may happen due to poor handling of several ART medications that patients have to take for 6 months as well patients may not disclose their HIV status and thus worried about keeping too many numbers of pills at home without being seen which may have an impact on their adherence [48]. The other reason may be, that patients may not be screened well using the criteria for ASM which patients may have an unseen deadly opportunistic infection like Tuberculosis, a cryptococcal infection that can take the life of such patients. Due to these reasons, patients may be lost from care.
LTFU was also more prevalent in individuals who had HVL in the follow-up. Patients with HVL were almost five times at increased risk of being lost. This is because patients with HVL in the follow-up period are more likely to have problems with adherence, psychosocial issues like fear of stigma, lack of social support, mental illness, substance abuse, poor livening condition, or even primary drug resistance, which have a direct or indirect effect on the continuity of care [43, 49].
This study has some limitations and strengths. Our research is innovative in that it uses routinely collected patient data available in many HIV programs in resource-limited settings, which allows for the model to be used across diverse backgrounds. The model is pragmatic in that it is a simple point-based model that can be calculated by various health care professional’s ranging from providers to adherence counselors. It can be easily adapted to mobile-app technology or the existing electronic medical record (smart care) so that HIV program could use this risk score to identify patients at the highest risk of LTFU after starting treatment and provide such patients with differentiated models of HIV care and interventions to reduce LTFU and the grave consequences following it.
Despite the strengths of this simple risk prediction score model, several limitations need to be acknowledged. First, though the model has good discrimination and calibration in the bootstrapped samples, the model should undergo external validation to see the performance of the risk prediction model/score in other populations. Second, is that we did not include determinants like monthly income, cigarette smoking, alcohol, and substance abuse, hemoglobin level, pain status, Hepatitis B and C status, baseline CD4 count, drug regimen, and adverse drug reactions which could have an impact on LTFU and maybe essential determinants for prediction of LTFU. Thus, the model prediction cannot be extended to such patients, limiting the model’s applicability. Third, the impact of Covid-19 on LTFU among HIV patients on ART was not assessed due to the retrospective nature of the study. Last, our study included people with a new and existing HIV diagnosis. People with a new HIV diagnosis may have different challenges like disclosure, fear of stigma, adherence issues, and modification of life to stay in care. Therefore, they may need a different risk score.