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Intensive care
Original research
Development and validation of a nomogram for predicting in-hospital mortality of elderly patients with persistent sepsis-associated acute kidney injury in intensive care units: a retrospective cohort study using the MIMIC-IV database
  1. Wei Jiang1,2,
  2. Chuanqing Zhang1,2,
  3. Jiangquan Yu1,2,
  4. Jun Shao1,2,
  5. Ruiqiang Zheng1,2
  1. 1Medical College, Yangzhou University, Yangzhou, China
  2. 2Intensive Care Unit, Northern Jiangsu People’s Hospital, Yangzhou, China
  1. Correspondence to Professor Ruiqiang Zheng; zhengruiqiang2021{at}163.com

Abstract

Objectives To identify the clinical risk factors that influence in-hospital mortality in elderly patients with persistent sepsis-associated acute kidney injury (S-AKI) and to establish and validate a nomogram to predict in-hospital mortality.

Design Retrospective cohort analysis.

Setting Data from critically ill patients at a US centre between 2008 and 2021 were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database (V.1.0).

Participants Data from 1519 patients with persistent S-AKI were extracted from the MIMIC-IV database.

Primary outcome All-cause in-hospital death from persistent S-AKI.

Results Multiple logistic regression revealed that gender (OR 0.63, 95% CI 0.45–0.88), cancer (2.5, 1.69–3.71), respiratory rate (1.06, 1.01–1.12), AKI stage (2.01, 1.24–3.24), blood urea nitrogen (1.01, 1.01–1.02), Glasgow Coma Scale score (0.75, 0.70–0.81), mechanical ventilation (1.57, 1.01–2.46) and continuous renal replacement therapy within 48 hours (9.97, 3.39–33.9) were independent risk factors for mortality from persistent S-AKI. The consistency indices of the prediction and the validation cohorts were 0.780 (95% CI: 0.75–0.82) and 0.80 (95% CI: 0.75–0.85), respectively. The model’s calibration plot suggested excellent consistency between the predicted and actual probabilities.

Conclusions This study’s prediction model demonstrated good discrimination and calibration abilities to predict in-hospital mortality of elderly patients with persistent S-AKI, although it warrants further external validation to verify its accuracy and applicability.

  • adult intensive & critical care
  • infectious diseases
  • acute renal failure

Data availability statement

Data are available upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

http://dx.doi.org/10.1136/bmjopen-2022-069824

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    STRENGTHS AND LIMITATIONS OF THIS STUDY

    • This study developed and validated a nomogram for predicting in-hospital mortality of elderly patients with persistent sepsis-associated acute kidney injury (AKI) in intensive care units.

    • Logistic regression was applied to analyse the clinical data, and the predictive models’ diagnostic efficacy was assessed using the consistency index, receiver operating characteristic curve and calibration plots.

    • Multiple imputation was used to handle the covariates with <20% missing to minimise the bias resulting from missing values.

    • This investigation solely used serum creatinine criteria to define AKI; oliguria may have led to the overlooking of some acute renal damage in patients.

    • The model was only internally validated, warranting further external validation to verify its accuracy and applicability.

    Introduction

    The world’s population is estimated to be >7.3 billion. Of this estimate, individuals aged ≥65 years would constitute approximately 9% of the population, which is expected to expand to 17% by 2050.1 Ageing has been associated with a decline in renal functions. Clinical studies have demonstrated that elderly patients have a higher risk of acute kidney injury (AKI) with a worsening prognosis.2–5 According to the US Renal Data System data from 2018, the in-hospital mortality for patients aged >66 years who were hospitalised for the first time with AKI was 8.2%, while that for inpatients without AKI was only 1.8%. On including the patients discharged to a hospice in the mortality calculation, the mortality of elderly inpatients with AKI and those without AKI increased by 13.2% and 3.8%, respectively.6 In addition, the mortality of elderly patients with AKI receiving dialysis treatment was higher, increasing from 31% to 80%.7 These statistics emphasise that elderly patients may encounter significant risks during AKI.

    The duration of AKI significantly influences an elderly patient’s prognosis. According to past studies, persistent acute renal damage, rather than transitory acute renal injury, is an independent risk factor for in-hospital mortality.8–11 In this study, the fatality of geriatric patients with persistent AKI was found to be as high as 53.1%, while the mortality of transient AKI was only 5.9%. Li et al12 conducted a retrospective analysis of elderly patients with AKI aged >75 years and found that elderly patients with persistent AKI were independently associated with a significantly higher 90-day mortality.

    Elderly patients with persistent sepsis-associated AKI (S-AKI) should therefore be paid special attention to in clinical management practices. Early and precise assessment of these patients’ mortality risk would facilitate early medical intervention and rational allocation of nursing resources, which, in turn, would improve patient survival through improved prognosis. The nomogram was deemed a robust tool for building a simplistic and intuitive prediction model for quantifying the perceived risk of clinical events.13 14 In order to investigate the risk factors for the poor short-term outcome and to provide a reference for the prevention and treatment of geriatric patients with persistent S-AKI, we developed a prediction model based on this nomogram for predicting in-hospital fatality among geriatric patients with persistent S-AKI.

    Methods

    Data source and preprocessing

    All data used in this study were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV V.1.0) database—a public-access database supported by the Department of Medicine at Beth Israel Deaconess Medical Center and the Computational Physiology Laboratory at MIT, which included full information of all patients admitted to the Beth Israel Deaconess Medical Center from 2008 to 2019 (last updated in March 2021).15 This database is freely accessible to any qualified PhysioNet user. The database consists of details of more than 500 000 hospital admissions and 70 000 intensive care unit (ICU) admissions.

    Study population

    In this analysis, all patients with S-AKI meeting Sepsis-3.0 and the Kidney Disease Improving Global Outcomes (KDIGO) creatinine criteria were included, in accordance with the ICD-9 (The International Statistical Classification of Diseases and Related Health Problems 9th Revision) diagnostic code of the database. The following were considered the inclusion criteria: sepsis diagnosed as an infection and Sequential Organ Failure Assessment score of ≥2.16 The diagnosis of AKI was serum creatinine (Scr) ≥0.3 mg/dL within 48 hours or Scr ≥50% within 7 days.17 Patients were excluded from the study if they had the following diseases: severe chronic kidney disease (defined as glomerular filtration rate <15 mL/min/1.73 m2), pregnancy, cardiac arrest, life expectancy <48 hours after the admission to the ICU and kidney transplantation.

    Clinical variables and definitions

    Several variables were extracted from the database, which comprised patient demographics, vital signs, medical history, laboratory tests and scoring systems. All data were collected within 24 hours of admission to the ICU. Other data collected included the requirement for mechanical ventilation and renal replacement therapy. The outcome measurements were the length of stay in the ICU, ICU mortality, the length of hospital stay and in-hospital mortality. The mean values of laboratory variables within 24 hours of ICU admission were used for analyses and included in the predictive model while considering that several variables were measured more than once. In the case of missing data, multiple imputation was employed for missing data on the biochemical parameters. For the missing data on height and weight, age and gender were used to layer the linear relationship for interpolation. For other classification variables, ‘null’ was used as the default value.

    According to the consensus report of the Acute Dialysis Quality Initiative 16 working group, persistent AKI was defined as duration of >48 hours, which meets the KDIGO standard.18 Transient AKI is defined as AKI with duration of <48 hours.

    Statistical analyses

    Qualitative variables were displayed by median (IQR) and categorical variables by frequency (n) in absolute numbers and percentage (%). Mann-Whitney U test, Fisher’s exact test or Χ2 test were employed for intergroup comparison, as deemed appropriate. First, univariate analyses were performed on all variables to determine the statistically significant factors affecting mortality. When using the logistic regression method for multivariate analyses, only variables with significant differences between the groups were used. Data were described by OR and CI of 95%, and p<0.05 was considered to indicate statistical significance. A clinical prediction model for predicting in-hospital mortality of persistent S-AKI was established through logistic regression, and the nomogram was evaluated by C statistics and operation area under the curve (AUC). All statistical analyses were conducted with the R V.4.2.1 software.

    Patient and public involvement

    None.

    Results

    Patient characteristics of the training and validation cohorts

    A total of 15 396 patients with sepsis in MIMIC-IV were screened sequentially with reference to the inclusion and exclusion criteria. A total of 4319 elderly patients with S-AKI were assessed, of which 1519 patients met the definition of persistent AKI and were accordingly assigned to either the training cohort (n=1065) or the validation cohort (n=454) (figure 1). No statistically significant difference was noted between the training and validation cohorts (online supplemental table 1).

    Figure 1
    Figure 1

    Study flow chart. AKI, acute kidney injury; ICU; intensive care unit; MIMIC-IV, Medical Information Mart for Intensive Care IV.

    Predictors and nomogram for in-hospital mortality

    Univariate logistic analysis was initially conducted for all variables to assess the independent risk factors for in-hospital mortality among geriatric patients with persistent S-AKI in the ICU (table 1). Univariately determined significant values (p<0.05) were then subjected to multiple logistic regression analyses, with the results presented as OR (95% CI). As presented in table 1, gender (0.63 (0.45–0.88)), cancer (2.50 (1.69–3.71)), respiratory rate (1.06 (1.01–1.12)), AKI stage (2.01 (1.24–3.24)), blood urea nitrogen (BUN) (1.01 (1.01–1.02)), Glasgow Coma Scale (GCS) score (0.75 (0.70–0.81)), mechanical ventilation (1.57 (1.01–2.46)) and continuous renal replacement therapy (CRRT) within 48 hours (9.97 (3.39–33.9)) were independently correlated with in-hospital death. Finally, we created a nomogram including the previously published predictive variables to predict the incidence of in-hospital death among geriatric individuals with persistent S-AKI (figure 2).

    View this table:
    Table 1

    Analysis of the risk factors for in-hospital mortality in the training cohort (univariate and multivariate logistic regression analyses)

    Figure 2
    Figure 2

    Nomogram for predicting in-hospital death of patients with persistent sepsis-associated AKI. AKI, acute kidney injury; BUN, blood urea nitrogen; CRRT, continuous renal replacement therapy; GCS, Glasgow Coma Scale.

    Evaluation and validation of the nomogram

    The consistency indices for the training and validation cohorts were applied for assessing the probability of in-hospital mortality for elderly patients with persistent S-AKI of 0.78 (95% CI: 0.75–0.81) and 0.80 (95% CI: 0.75–0.85), respectively. In the training cohort, the AUC of the nomogram predicting the in-hospital risk of death was 0.78 (95% CI: 0.75–0.82) (figure 3A) in the training cohort and 0.82 (95% CI: 0.77–0.87) in the validation cohort (figure 3B). The calibration curve indicated that the nomogram model’s predicted results were in excellent accordance with the actual observations (figure 4).

    Figure 3
    Figure 3

    ROC of nomogram in training cohort (A) and validation cohort (B). AUC, area under the curve; ROC, receiver operating characteristic.

    Figure 4
    Figure 4

    Calibration curve for patients with persistent sepsis-associated acute kidney injury is based on a nomogram.

    Discussion

    Based on the current findings, obtained through both univariate and multivariate regression analyses, the risks of in-hospital death among geriatric patients with persistent S-AKI were associated with gender, AKI stage, BUN, GCS score, cancer, respiratory rate, CRRT within 48 hours and mechanical ventilation. In addition, we developed a nomogram to predict the near-term outcomes in patients with persistent S-AKI, and it performed well on both the training and external validation datasets. This is the first research to develop and validate an in-hospital mortality risk prediction model for geriatric patients with persistent S-AKI that allows a simple, but relatively accurate risk-identifying tool for early detection and response.

    Clinical research demonstrated that geriatric individuals were more likely to develop AKI and demonstrated a poorer prognosis following an episode of AKI.2–5 Sepsis and prolonged AKI duration significantly increased all-cause mortality in elderly patients during hospitalisation.12 19 20 Geriatric individuals with persistent S-AKI had a significantly higher frequency of in-hospital mortality and ICU mortality when compared with patients with AKI with shorter duration of renal impairment or without sepsis.11 20 Emphasising sepsis management or renal support therapy alone does not significantly reduce mortality in elderly patients with persistent S-AKI.21 Therefore, we focused on elderly patients with persistent S-AKI, analysed the risk factors correlated with in-hospital mortality in this segment of patients and developed a nomogram to predict persistent S-AKI, which has the potential to improve the short-term outcomes of persistent S-AKI.

    The present results demonstrated that the severity of AKI is a significant risk factor for increased all-cause mortality in geriatric individuals with persistent S-AKI (OR=2.01). Our study is supported by data from past studies suggesting that the overall in-hospital mortality rate without AKI was only 0.6%, while that in patients with AKI was significantly higher; the higher the stage of AKI, the greater the mortality rate (5.3% for stage I AKI, 13.4% for stage III AKI and 35.4% for stage III AKI).22 23 Therefore, the near-term prognosis of elderly patients with persistent S-AKI may be assessed and predicted in clinical practice based on the stage of AKI.

    The metric GCS score is commonly used to evaluate the degree of consciousness in patients with sepsis.24 A previous meta-analysis revealed that patients with AKI following trauma often exhibited low GCS scores.25 In addition, a correlation was noted between the GCS scores and in-hospital mortality in elderly patients with sepsis.26 A higher GCS score was also recorded as a risk factor for in-hospital mortality in elderly patients with persistent S-AKI.

    The role of gender in kidney diseases remains a topic of widespread interest. Gender differences in chronic disease and mortality persist with population-based studies, suggesting that women have a higher overall prevalence of chronic kidney diseases.27–29 However, men are twice as likely as women to develop kidney cancer, with a higher mortality rate.30 In addition, exogenous hormone therapy has been associated with an increase in the incidence of AKI in patients with prostate cancer.31 Although our study only included patients with acute diseases, our findings implied that sex-related effects may play a role in the clinical course of S-AKI, which must be considered clinically.

    Indeed, the risk of AKI increases in the first year following cancer diagnosis among elderly patients with cancer; this composite harms their survival, with a significant increase in their potential mortality rate.32 Past studies have demonstrated that BUN predicts both short-term and long-term mortality independently of the Scr levels.33 Pre-dialysis BUN levels predicted 60-day mortality in individuals with severe AKI requiring dialysis.34 We found that the BUN levels acted as an independent predictor of short-term mortality.

    In our study, mechanical ventilation and CRRT within 48 hours acted as independent predictors of in-hospital mortality in older adults with S-AKI. In a recent meta-analysis, van den Akker et al recorded an association between invasive mechanical ventilation and a threefold higher risk of AKI.35 Clinical studies have demonstrated that hypoxaemia, hypercapnia, high positive end-expiratory pressure values and high tidal volume are the risk predictors for AKI in people on mechanical ventilation.35 Past evidence indicates that mechanical ventilation is related to the incidence rate, risk factors, all-cause mortality and renal prognosis of AKI in elderly patients.36 37CRRT is an important treatment modality for AKI in critically ill patients, with an increasing number of patients having received CRRT.38 39 In recent studies, CRRT has been reported to significantly increase all-cause death rates.38 39

    Finally, the in-hospital mortality prediction model proposed in this report displayed good discriminative power in training and external test sets. Owing to its simplicity, it lends itself well to generalisation and application in clinical care and treatment. This study, however, has some limitations. First, we only internally verified the model, and the result warrants external confirmation. Second, because this investigation solely used Scr criteria to define AKI, oliguria may have led to overlooking some acute renal damage in patients. Third, the current study may have missed some potential risk factors, such as the Renal Resistance Index and new biomarkers of CCL14. Thus, the prediction model requires further clinical data for assessment as well as external validation to verify its accuracy and applicability.

    Conclusion

    Our study showed that CRRT within 48 hours, mechanical ventilation, cancer, respiratory rate, gender, AKI stage, BUN and GCS score acted as independent predictors of in-hospital mortality among elderly patients with persistent S-AKI. We also showed that the resulting nomogram had good predictive performance, although it warrants further external validation to verify its accuracy and applicability.

    Data availability statement

    Data are available upon reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    Considering that this study was based on the analysis of an anonymous third-party public database with prior approval from the Institutional Review Board, no ethical review was required.

    Acknowledgments

    We would like to thank the participants, developers and investigators associated with the Medical Information Mart for Intensive Care (MIMIC)-IV database.

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    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    Footnotes

    • WJ and CZ contributed equally.

    • Contributors All the authors participated in literature retrieval and viewpoint discussion in this article. WJ and CZ are the main contributors to data extraction, data analysis and writing. RZ, JY, and JS revised this article. All authors have read and approved the final manuscript. RZ is responsible for all the study work as the guarantor.

    • Funding This work was supported by Jiangsu Province's '333 Projects' (BRA2020183), Top-notch Talents of 'Six-one Projects' of Jiangsu Commission of Health (LGY2019036), scientific research project of Jiangsu Commission of Health (Z2020055) and Yangzhou Social Development Project (YZ2021059).

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

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